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class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" alt="Marcie Bockbrader" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/7626683/8794317/9819623/s200_marcie.bockbrader.jpg" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Marcie Bockbrader</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://osu.academia.edu/">Ohio State University</a>, <a class="u-tcGrayDarker" href="https://osu.academia.edu/Departments/Physical_Medicine_and_Rehabilitation/Documents">Physical Medicine and Rehabilitation</a>, <span class="u-tcGrayDarker">Faculty Member</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" 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data-has-card-for-ri-list="7626683">View All (8)</a></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="7626683" href="https://www.academia.edu/Documents/in/Physical_Medicine_and_Rehabilitation"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://osu.academia.edu/MarcieBockbrader","location":"/MarcieBockbrader","scheme":"https","host":"osu.academia.edu","port":null,"pathname":"/MarcieBockbrader","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Physical Medicine and Rehabilitation"]}" data-trace="false" data-dom-id="Pill-react-component-2ef42c99-a02b-49de-85e5-bb9b54e587bc"></div> <div id="Pill-react-component-2ef42c99-a02b-49de-85e5-bb9b54e587bc"></div> </a><a 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<div id="Pill-react-component-c8461159-e628-4e38-a8dc-2f191dfd0d4e"></div> </a></div></div><div class="external-links-container"><ul class="profile-links new-profile js-UserInfo-social"><li class="profile-profiles js-social-profiles-container"><i class="fa fa-spin fa-spinner"></i></li></ul></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="nav-container backbone-profile-documents-nav hidden-xs"><ul class="nav-tablist" role="tablist"><li class="nav-chip active" role="presentation"><a data-section-name="" data-toggle="tab" href="#all" role="tab">all</a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Papers" data-toggle="tab" href="#papers" role="tab" title="Papers"><span>39</span> <span class="ds2-5-body-sm-bold">Papers</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Conference-Presentations" data-toggle="tab" href="#conferencepresentations" role="tab" title="Conference Presentations"><span>1</span> <span class="ds2-5-body-sm-bold">Conference Presentations</span></a></li></ul></div><div class="divider ds-divider-16" style="margin: 0px;"></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Marcie Bockbrader</h3></div><div class="js-work-strip profile--work_container" data-work-id="83414563"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83414563/2091896_Can_Medical_Students_Achieve_Minimal_Competency_in_MSK_Ultrasound"><img alt="Research paper thumbnail of 2091896 Can Medical Students Achieve Minimal Competency in MSK Ultrasound?" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83414563/2091896_Can_Medical_Students_Achieve_Minimal_Competency_in_MSK_Ultrasound">2091896 Can Medical Students Achieve Minimal Competency in MSK Ultrasound?</a></div><div class="wp-workCard_item"><span>Ultrasound in Medicine & Biology</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span 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href="https://www.academia.edu/40495385/Upper_limb_sensorimotor_restoration_through_brain_computer_interface_technology_in_tetraparesis"><img alt="Research paper thumbnail of Upper limb sensorimotor restoration through brain-computer interface technology in tetraparesis" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40495385/Upper_limb_sensorimotor_restoration_through_brain_computer_interface_technology_in_tetraparesis">Upper limb sensorimotor restoration through brain-computer interface technology in tetraparesis</a></div><div class="wp-workCard_item"><span>Current Opinion in Biomedical Engineering</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">For individuals with spinal cord injury (SCI), brain-computer interface (BCI) technology offers a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">For individuals with spinal cord injury (SCI), brain-computer interface (BCI) technology offers a means to restore lost sensorimotor function by bridging disrupted neural pathways to reanimate paralyzed limbs. Restoring hand function is a high priority of those with tetraparesis due to the impact that manual dexterity has on independence and quality of life. However, to be useful in daily life, BCI systems need to deliver naturalistic and functional grasp speed, force, and dexterity. In clinical trials, individuals with paralysis have achieved the most dexterous control of grasp using either robotic neuroprosthetics or neuromuscular stimulation orthotics controlled by intracortical BCI systems. Next steps are in progress, with the development of portable components and decoding algorithm optimization to simplify setup and calibration.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40495385"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40495385"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40495385; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40495385]").text(description); $(".js-view-count[data-work-id=40495385]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40495385; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40495385']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40495385, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=40495385]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40495385,"title":"Upper limb sensorimotor restoration through brain-computer interface technology in tetraparesis","translated_title":"","metadata":{"doi":"10.1016/j.cobme.2019.09.002","abstract":"For individuals with spinal cord injury (SCI), brain-computer interface (BCI) technology offers a means to restore lost sensorimotor function by bridging disrupted neural pathways to reanimate paralyzed limbs. Restoring hand function is a high priority of those with tetraparesis due to the impact that manual dexterity has on independence and quality of life. However, to be useful in daily life, BCI systems need to deliver naturalistic and functional grasp speed, force, and dexterity. In clinical trials, individuals with paralysis have achieved the most dexterous control of grasp using either robotic neuroprosthetics or neuromuscular stimulation orthotics controlled by intracortical BCI systems. Next steps are in progress, with the development of portable components and decoding algorithm optimization to simplify setup and calibration.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Current Opinion in Biomedical Engineering"},"translated_abstract":"For individuals with spinal cord injury (SCI), brain-computer interface (BCI) technology offers a means to restore lost sensorimotor function by bridging disrupted neural pathways to reanimate paralyzed limbs. Restoring hand function is a high priority of those with tetraparesis due to the impact that manual dexterity has on independence and quality of life. However, to be useful in daily life, BCI systems need to deliver naturalistic and functional grasp speed, force, and dexterity. In clinical trials, individuals with paralysis have achieved the most dexterous control of grasp using either robotic neuroprosthetics or neuromuscular stimulation orthotics controlled by intracortical BCI systems. Next steps are in progress, with the development of portable components and decoding algorithm optimization to simplify setup and calibration.","internal_url":"https://www.academia.edu/40495385/Upper_limb_sensorimotor_restoration_through_brain_computer_interface_technology_in_tetraparesis","translated_internal_url":"","created_at":"2019-10-01T18:49:27.857-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Upper_limb_sensorimotor_restoration_through_brain_computer_interface_technology_in_tetraparesis","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[],"research_interests":[{"id":22824,"name":"Spinal Cord Injury","url":"https://www.academia.edu/Documents/in/Spinal_Cord_Injury"},{"id":46043,"name":"Functional Electrical Stimulation","url":"https://www.academia.edu/Documents/in/Functional_Electrical_Stimulation"},{"id":47558,"name":"BCI","url":"https://www.academia.edu/Documents/in/BCI"},{"id":84493,"name":"Neuroprosthetics","url":"https://www.academia.edu/Documents/in/Neuroprosthetics"}],"urls":[{"id":8862047,"url":"https://doi.org/10.1016/j.cobme.2019.09.002"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049465"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049465/Neural_Decoding_Algorithm_Requirements_for_a_Take_home_Brain_Computer_Interface"><img alt="Research paper thumbnail of Neural Decoding Algorithm Requirements for a Take-home Brain Computer Interface" class="work-thumbnail" src="https://attachments.academia-assets.com/60248868/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049465/Neural_Decoding_Algorithm_Requirements_for_a_Take_home_Brain_Computer_Interface">Neural Decoding Algorithm Requirements for a Take-home Brain Computer Interface</a></div><div class="wp-workCard_item"><span>Conf Proc IEEE Eng Med Biol Soc</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Brain computer interfaces (BCIs) have had several successful laboratory demonstrations, raising h...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Brain computer interfaces (BCIs) have had several successful laboratory demonstrations, raising hopes that a take-home system could improve the lives of patients in the future. However, challenges remain in translating BCI control of an assistive device in the lab into a robust take-home system. One challenge is designing neural decoders, algorithms that translate neural activity into control commands for a device, that meet BCI systems and extract requirements for neural decoding. Translating laboratory demonstrations of BCI systems to home-use requires careful consideration of patient priorities and signficant technical challenges. To address the former, potential users were surveyed on several BCI characteristics. We examine two such surveys and extract requirements for the technical challenge of BCI decoding, the algorithms that translate brain activity into actions. In one survey, potential users ranked non-invasiveness, daily setup time, independent operation, cost, number of functions provided, and response time as [1]. In another, the number of functions, simplicity of setup, accuracy, electrode type, setup time, and speed were all ranked with a median importance of least 9 out of 10 [2]. The characteristics directly impacted by the decoding algorithm fall into four main categories: setup time related to decoder training, number of functions provided, response time, and accuracy. The decoder-related setup time is primarily the time spent by the user calibrating the decoding algorithm to account for any day-today variability in the neural signals. It is typically performed on a pre-defined task where data is collected to train or update the decoding algorithms as the user performs the guided task. In [1] users expressed willingness to spend more time during initial training but want to minimize or even eliminate daily decoder setup time. Overall, a total setup time of 10-20 minutes would satisfy 65% of potential users, however that includes setup time not related to decoding [2].</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9f6f574cc6d2b2fd6258372315d4be65" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248868,"asset_id":40049465,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248868/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049465"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049465"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049465; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049465]").text(description); $(".js-view-count[data-work-id=40049465]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049465; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049465']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049465, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9f6f574cc6d2b2fd6258372315d4be65" } } $('.js-work-strip[data-work-id=40049465]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049465,"title":"Neural Decoding Algorithm Requirements for a Take-home Brain Computer Interface","translated_title":"","metadata":{"abstract":"Brain computer interfaces (BCIs) have had several successful laboratory demonstrations, raising hopes that a take-home system could improve the lives of patients in the future. However, challenges remain in translating BCI control of an assistive device in the lab into a robust take-home system. One challenge is designing neural decoders, algorithms that translate neural activity into control commands for a device, that meet BCI systems and extract requirements for neural decoding. Translating laboratory demonstrations of BCI systems to home-use requires careful consideration of patient priorities and signficant technical challenges. To address the former, potential users were surveyed on several BCI characteristics. We examine two such surveys and extract requirements for the technical challenge of BCI decoding, the algorithms that translate brain activity into actions. In one survey, potential users ranked non-invasiveness, daily setup time, independent operation, cost, number of functions provided, and response time as [1]. In another, the number of functions, simplicity of setup, accuracy, electrode type, setup time, and speed were all ranked with a median importance of least 9 out of 10 [2]. The characteristics directly impacted by the decoding algorithm fall into four main categories: setup time related to decoder training, number of functions provided, response time, and accuracy. The decoder-related setup time is primarily the time spent by the user calibrating the decoding algorithm to account for any day-today variability in the neural signals. It is typically performed on a pre-defined task where data is collected to train or update the decoding algorithms as the user performs the guided task. In [1] users expressed willingness to spend more time during initial training but want to minimize or even eliminate daily decoder setup time. Overall, a total setup time of 10-20 minutes would satisfy 65% of potential users, however that includes setup time not related to decoding [2].","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Conf Proc IEEE Eng Med Biol Soc"},"translated_abstract":"Brain computer interfaces (BCIs) have had several successful laboratory demonstrations, raising hopes that a take-home system could improve the lives of patients in the future. However, challenges remain in translating BCI control of an assistive device in the lab into a robust take-home system. One challenge is designing neural decoders, algorithms that translate neural activity into control commands for a device, that meet BCI systems and extract requirements for neural decoding. Translating laboratory demonstrations of BCI systems to home-use requires careful consideration of patient priorities and signficant technical challenges. To address the former, potential users were surveyed on several BCI characteristics. We examine two such surveys and extract requirements for the technical challenge of BCI decoding, the algorithms that translate brain activity into actions. In one survey, potential users ranked non-invasiveness, daily setup time, independent operation, cost, number of functions provided, and response time as [1]. In another, the number of functions, simplicity of setup, accuracy, electrode type, setup time, and speed were all ranked with a median importance of least 9 out of 10 [2]. The characteristics directly impacted by the decoding algorithm fall into four main categories: setup time related to decoder training, number of functions provided, response time, and accuracy. The decoder-related setup time is primarily the time spent by the user calibrating the decoding algorithm to account for any day-today variability in the neural signals. It is typically performed on a pre-defined task where data is collected to train or update the decoding algorithms as the user performs the guided task. In [1] users expressed willingness to spend more time during initial training but want to minimize or even eliminate daily decoder setup time. Overall, a total setup time of 10-20 minutes would satisfy 65% of potential users, however that includes setup time not related to decoding [2].","internal_url":"https://www.academia.edu/40049465/Neural_Decoding_Algorithm_Requirements_for_a_Take_home_Brain_Computer_Interface","translated_internal_url":"","created_at":"2019-08-09T15:20:26.061-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32893001,"work_id":40049465,"tagging_user_id":7626683,"tagged_user_id":47636515,"co_author_invite_id":null,"email":"f***d@battelle.org","display_order":1,"name":"David Friedenberg","title":"Neural Decoding Algorithm Requirements for a Take-home Brain Computer Interface"}],"downloadable_attachments":[{"id":60248868,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248868/thumbnails/1.jpg","file_name":"EMBC18_0396_FI20190809-120387-19sge6m.pdf","download_url":"https://www.academia.edu/attachments/60248868/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Neural_Decoding_Algorithm_Requirements_f.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248868/EMBC18_0396_FI20190809-120387-19sge6m-libre.pdf?1565391633=\u0026response-content-disposition=attachment%3B+filename%3DNeural_Decoding_Algorithm_Requirements_f.pdf\u0026Expires=1732437019\u0026Signature=IR1SLy3U8jovt~IQoCLlDCBA1YKvyBnfIQ2a~IHCO~94fmrfUU6Tr4yT8hZuiUuUZy5tod1Y91BtanMaDP4tMcOjnfzic6Qv3x~fDDx9FnGHbmvMSa5gyvWxfvW9HgCEk-ILcz-IfHNv63bCHDUtOnFbNI-xuK4xo1qXrHmH9Ui3yUtODhXdQQxyZ2grZ4rDbRsFlvebfkfS4BzsGVhIWuSYM5K7fncneJ-jpuPs3BTRKv2DpzCVgxbeZDVjyr~VKTT-6MWck8OSrYGZR69mz4RRO~-wPo3jT-WzMm6CthAcLdUhe4xkAhfG3tEjaeU6n-4xVpw3-ivCxFGHBtQ8CQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Neural_Decoding_Algorithm_Requirements_for_a_Take_home_Brain_Computer_Interface","translated_slug":"","page_count":1,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248868,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248868/thumbnails/1.jpg","file_name":"EMBC18_0396_FI20190809-120387-19sge6m.pdf","download_url":"https://www.academia.edu/attachments/60248868/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Neural_Decoding_Algorithm_Requirements_f.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248868/EMBC18_0396_FI20190809-120387-19sge6m-libre.pdf?1565391633=\u0026response-content-disposition=attachment%3B+filename%3DNeural_Decoding_Algorithm_Requirements_f.pdf\u0026Expires=1732437019\u0026Signature=IR1SLy3U8jovt~IQoCLlDCBA1YKvyBnfIQ2a~IHCO~94fmrfUU6Tr4yT8hZuiUuUZy5tod1Y91BtanMaDP4tMcOjnfzic6Qv3x~fDDx9FnGHbmvMSa5gyvWxfvW9HgCEk-ILcz-IfHNv63bCHDUtOnFbNI-xuK4xo1qXrHmH9Ui3yUtODhXdQQxyZ2grZ4rDbRsFlvebfkfS4BzsGVhIWuSYM5K7fncneJ-jpuPs3BTRKv2DpzCVgxbeZDVjyr~VKTT-6MWck8OSrYGZR69mz4RRO~-wPo3jT-WzMm6CthAcLdUhe4xkAhfG3tEjaeU6n-4xVpw3-ivCxFGHBtQ8CQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":193254,"name":"SVM classifier","url":"https://www.academia.edu/Documents/in/SVM_classifier"},{"id":644153,"name":"Brain Computer Interface BCI","url":"https://www.academia.edu/Documents/in/Brain_Computer_Interface_BCI"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049456"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049456/Clinical_performance_evaluation_for_a_take_home_brain_computer_interface_for_grasp"><img alt="Research paper thumbnail of Clinical performance evaluation for a take-home brain computer interface for grasp" class="work-thumbnail" src="https://attachments.academia-assets.com/60248830/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049456/Clinical_performance_evaluation_for_a_take_home_brain_computer_interface_for_grasp">Clinical performance evaluation for a take-home brain computer interface for grasp</a></div><div class="wp-workCard_item"><span>Conf Proc IEEE Eng Med Biol Soc</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Brain computer interfaces (BCIs) have successfully been used in laboratory settings to restore up...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Brain computer interfaces (BCIs) have successfully been used in laboratory settings to restore upper limb motor function to individuals paralyzed from spinal cord injury. However, translation into neuroprosthetics for home use requires optimization informed by patient-centered design. Here, we review patient priorities from the literature and describe GAIN, a patient-centric framework for evaluating clinical performance of BCI-grasp neuroprosthetics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="781cefb630ac8d06425a3f2210d457c0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248830,"asset_id":40049456,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248830/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049456"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049456"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049456; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049456]").text(description); $(".js-view-count[data-work-id=40049456]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049456; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049456']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049456, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "781cefb630ac8d06425a3f2210d457c0" } } $('.js-work-strip[data-work-id=40049456]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049456,"title":"Clinical performance evaluation for a take-home brain computer interface for grasp","translated_title":"","metadata":{"abstract":"Brain computer interfaces (BCIs) have successfully been used in laboratory settings to restore upper limb motor function to individuals paralyzed from spinal cord injury. However, translation into neuroprosthetics for home use requires optimization informed by patient-centered design. Here, we review patient priorities from the literature and describe GAIN, a patient-centric framework for evaluating clinical performance of BCI-grasp neuroprosthetics.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Conf Proc IEEE Eng Med Biol Soc"},"translated_abstract":"Brain computer interfaces (BCIs) have successfully been used in laboratory settings to restore upper limb motor function to individuals paralyzed from spinal cord injury. However, translation into neuroprosthetics for home use requires optimization informed by patient-centered design. Here, we review patient priorities from the literature and describe GAIN, a patient-centric framework for evaluating clinical performance of BCI-grasp neuroprosthetics.","internal_url":"https://www.academia.edu/40049456/Clinical_performance_evaluation_for_a_take_home_brain_computer_interface_for_grasp","translated_internal_url":"","created_at":"2019-08-09T15:18:37.056-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":60248830,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248830/thumbnails/1.jpg","file_name":"EMBC18_0494_FI20190809-73782-ne811i.pdf","download_url":"https://www.academia.edu/attachments/60248830/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Clinical_performance_evaluation_for_a_ta.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248830/EMBC18_0494_FI20190809-73782-ne811i-libre.pdf?1565391640=\u0026response-content-disposition=attachment%3B+filename%3DClinical_performance_evaluation_for_a_ta.pdf\u0026Expires=1732437019\u0026Signature=eZeTG1n9EFDf9vQpSTME5KcMA1xgqxZywMvvIOfES6ciW-oYfInVjFMwXrqeYKpsl3DCaC9JyODbi2OdrTEl6qhKzHpmIPnVszc-S98IEb3Im-z24xl~9-jd7fIL7Q85FTDZD2DQmopAj57UUk-ywrzDzr6lqtaPk21c3NxVVNTUtW0UYfWiMYarm0k3XQAbTvhe2KR4r8Pg1CMncSeFjwCzKpEiZ2VB23j8saEKXGZYEnd6VKjSUFpIiba0w7WuCWjxe~EZPMk-cFr-DIdaDDqryNE0ISpHjMu7e5UdyzhKohi59aE6GIEgZ8T~-wLsLzLXmMJcSOprrZRvs~zB6A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Clinical_performance_evaluation_for_a_take_home_brain_computer_interface_for_grasp","translated_slug":"","page_count":1,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248830,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248830/thumbnails/1.jpg","file_name":"EMBC18_0494_FI20190809-73782-ne811i.pdf","download_url":"https://www.academia.edu/attachments/60248830/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Clinical_performance_evaluation_for_a_ta.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248830/EMBC18_0494_FI20190809-73782-ne811i-libre.pdf?1565391640=\u0026response-content-disposition=attachment%3B+filename%3DClinical_performance_evaluation_for_a_ta.pdf\u0026Expires=1732437019\u0026Signature=eZeTG1n9EFDf9vQpSTME5KcMA1xgqxZywMvvIOfES6ciW-oYfInVjFMwXrqeYKpsl3DCaC9JyODbi2OdrTEl6qhKzHpmIPnVszc-S98IEb3Im-z24xl~9-jd7fIL7Q85FTDZD2DQmopAj57UUk-ywrzDzr6lqtaPk21c3NxVVNTUtW0UYfWiMYarm0k3XQAbTvhe2KR4r8Pg1CMncSeFjwCzKpEiZ2VB23j8saEKXGZYEnd6VKjSUFpIiba0w7WuCWjxe~EZPMk-cFr-DIdaDDqryNE0ISpHjMu7e5UdyzhKohi59aE6GIEgZ8T~-wLsLzLXmMJcSOprrZRvs~zB6A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":18450,"name":"Neuromuscular Control","url":"https://www.academia.edu/Documents/in/Neuromuscular_Control"},{"id":22824,"name":"Spinal Cord Injury","url":"https://www.academia.edu/Documents/in/Spinal_Cord_Injury"},{"id":46043,"name":"Functional Electrical Stimulation","url":"https://www.academia.edu/Documents/in/Functional_Electrical_Stimulation"},{"id":644153,"name":"Brain Computer Interface BCI","url":"https://www.academia.edu/Documents/in/Brain_Computer_Interface_BCI"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049317"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049317/Brain_Computer_Interfaces_in_Rehabilitation_Medicine"><img alt="Research paper thumbnail of Brain Computer Interfaces in Rehabilitation Medicine" class="work-thumbnail" src="https://attachments.academia-assets.com/60248794/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049317/Brain_Computer_Interfaces_in_Rehabilitation_Medicine">Brain Computer Interfaces in Rehabilitation Medicine</a></div><div class="wp-workCard_item"><span>PM&R</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One innovation currently influencing physical medicine and rehabilitation is brainecomputer inter...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One innovation currently influencing physical medicine and rehabilitation is brainecomputer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a6b8307d093e3543b03febdae816f666" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248794,"asset_id":40049317,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248794/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049317"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049317"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049317; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049317]").text(description); $(".js-view-count[data-work-id=40049317]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049317; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049317']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049317, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a6b8307d093e3543b03febdae816f666" } } $('.js-work-strip[data-work-id=40049317]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049317,"title":"Brain Computer Interfaces in Rehabilitation Medicine","translated_title":"","metadata":{"doi":"10.1016/j.pmrj.2018.05.028","abstract":"One innovation currently influencing physical medicine and rehabilitation is brainecomputer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"PM\u0026R"},"translated_abstract":"One innovation currently influencing physical medicine and rehabilitation is brainecomputer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.","internal_url":"https://www.academia.edu/40049317/Brain_Computer_Interfaces_in_Rehabilitation_Medicine","translated_internal_url":"","created_at":"2019-08-09T15:11:48.234-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892999,"work_id":40049317,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":1,"name":"Marcia Bockbrader","title":"Brain Computer Interfaces in Rehabilitation Medicine"}],"downloadable_attachments":[{"id":60248794,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248794/thumbnails/1.jpg","file_name":"Bockbrader_etal2018_BCIinRehab20190809-29399-q687av.pdf","download_url":"https://www.academia.edu/attachments/60248794/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Brain_Computer_Interfaces_in_Rehabilitat.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248794/Bockbrader_etal2018_BCIinRehab20190809-29399-q687av-libre.pdf?1565390551=\u0026response-content-disposition=attachment%3B+filename%3DBrain_Computer_Interfaces_in_Rehabilitat.pdf\u0026Expires=1732437019\u0026Signature=aaWj2usKUxQBpio1fj446e6Ugfk6riLdkbXnR-w4JNJB~1D78JCCds0-hhuwrjxbX7SbpNZA7ltkzIDUYXh-jbqRo0mh9cM3DUzPJa6HIC3xgray0qkB-oRQkRGcJGIJiAPzFAzTf8l0O7kxPXXqIr4KObfCbfaQZPbpxKCjefsdhgxKWQuT3NI2~qrw1ReiZe7TpOTPof3rcMxgilqJfVCAYJ9Sg4abO7V1EgxRmgG2AHo4vF1GXp4aVGJu9wqgCCJ4tvylbg0sz78XNYgQU2DkK6pwAXYYaWcWiKJRm-bpZkUiCKpI58lyNJU84X9zYR~Qef00pDUMBRtQN-NqFw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Brain_Computer_Interfaces_in_Rehabilitation_Medicine","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248794,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248794/thumbnails/1.jpg","file_name":"Bockbrader_etal2018_BCIinRehab20190809-29399-q687av.pdf","download_url":"https://www.academia.edu/attachments/60248794/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Brain_Computer_Interfaces_in_Rehabilitat.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248794/Bockbrader_etal2018_BCIinRehab20190809-29399-q687av-libre.pdf?1565390551=\u0026response-content-disposition=attachment%3B+filename%3DBrain_Computer_Interfaces_in_Rehabilitat.pdf\u0026Expires=1732437019\u0026Signature=aaWj2usKUxQBpio1fj446e6Ugfk6riLdkbXnR-w4JNJB~1D78JCCds0-hhuwrjxbX7SbpNZA7ltkzIDUYXh-jbqRo0mh9cM3DUzPJa6HIC3xgray0qkB-oRQkRGcJGIJiAPzFAzTf8l0O7kxPXXqIr4KObfCbfaQZPbpxKCjefsdhgxKWQuT3NI2~qrw1ReiZe7TpOTPof3rcMxgilqJfVCAYJ9Sg4abO7V1EgxRmgG2AHo4vF1GXp4aVGJu9wqgCCJ4tvylbg0sz78XNYgQU2DkK6pwAXYYaWcWiKJRm-bpZkUiCKpI58lyNJU84X9zYR~Qef00pDUMBRtQN-NqFw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":2256,"name":"Rehabilitation","url":"https://www.academia.edu/Documents/in/Rehabilitation"},{"id":3946,"name":"Neurorehabilitation","url":"https://www.academia.edu/Documents/in/Neurorehabilitation"},{"id":32001,"name":"Physical Medicine and Rehabilitation","url":"https://www.academia.edu/Documents/in/Physical_Medicine_and_Rehabilitation"},{"id":256326,"name":"Neurotechnology","url":"https://www.academia.edu/Documents/in/Neurotechnology"},{"id":644153,"name":"Brain Computer Interface BCI","url":"https://www.academia.edu/Documents/in/Brain_Computer_Interface_BCI"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049306"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049306/Does_Ultrasound_Enhanced_Instruction_of_Musculoskeletal_Anatomy_Improve_Physical_Examination_Skills_of_First_Year_Medical_Students"><img alt="Research paper thumbnail of Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical Students" class="work-thumbnail" src="https://attachments.academia-assets.com/60248785/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049306/Does_Ultrasound_Enhanced_Instruction_of_Musculoskeletal_Anatomy_Improve_Physical_Examination_Skills_of_First_Year_Medical_Students">Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical Students</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://osu.academia.edu/MarcieBockbrader">Marcie Bockbrader</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://osu1.academia.edu/DavidWay">David Way</a></span></div><div class="wp-workCard_item"><span>Journal of Ultrasound in Medicine</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objectives-Ultrasound imaging is commonly used to teach basic anatomy to medical students. The pu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objectives-Ultrasound imaging is commonly used to teach basic anatomy to medical students. The purpose of this study was to determine whether learning musculo-skeletal anatomy with ultrasound improved performance on medical students' musculoskeletal physical examination skills. Methods-Twenty-seven first-year medical students were randomly assigned to 1 of 2 instructional groups: either shoulder or knee. Both groups received a lecture followed by hands-on ultrasound scanning on live human models of the assigned joint. After instruction, students were assessed on their ability to accurately palpate 4 ana-tomic landmarks: the acromioclavicular joint, the proximal long-head biceps tendon, and the medial and lateral joint lines of the knee. Performance scores were based on both accuracy and time. A total physical examination performance score was derived for each joint. Scores for instructional groups were compared by a 2-way analysis of variance with 1 repeated measure. Significant findings were further analyzed with post hoc tests. Results-All students performed significantly better on the knee examination, irrespective of instructional group (F 5 14.9; df 5 1.25; P 5 .001). Moreover, the shoulder instruction group performed significantly better than the knee group on the overall assessment (t 5-3.0; df 5 25; P < .01). Post hoc analyses revealed that differences in group performance were due to the shoulder group's higher scores on palpation of the biceps tendon (t 5-2.8; df 5 25; P 5 .01), a soft tissue landmark. Both groups performed similarly on palpation of all other anatomic structures. Conclusions-The use of ultrasound appears to provide an educational advantage when learning musculoskeletal physical examination of soft tissue landmarks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2b3ec1036c1ed9a61e70fb988facc055" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248785,"asset_id":40049306,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248785/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049306"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049306"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049306; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049306]").text(description); $(".js-view-count[data-work-id=40049306]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049306; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049306']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049306, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2b3ec1036c1ed9a61e70fb988facc055" } } $('.js-work-strip[data-work-id=40049306]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049306,"title":"Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical Students","translated_title":"","metadata":{"doi":"10.1002/jum.14322","abstract":"Objectives-Ultrasound imaging is commonly used to teach basic anatomy to medical students. The purpose of this study was to determine whether learning musculo-skeletal anatomy with ultrasound improved performance on medical students' musculoskeletal physical examination skills. Methods-Twenty-seven first-year medical students were randomly assigned to 1 of 2 instructional groups: either shoulder or knee. Both groups received a lecture followed by hands-on ultrasound scanning on live human models of the assigned joint. After instruction, students were assessed on their ability to accurately palpate 4 ana-tomic landmarks: the acromioclavicular joint, the proximal long-head biceps tendon, and the medial and lateral joint lines of the knee. Performance scores were based on both accuracy and time. A total physical examination performance score was derived for each joint. Scores for instructional groups were compared by a 2-way analysis of variance with 1 repeated measure. Significant findings were further analyzed with post hoc tests. Results-All students performed significantly better on the knee examination, irrespective of instructional group (F 5 14.9; df 5 1.25; P 5 .001). Moreover, the shoulder instruction group performed significantly better than the knee group on the overall assessment (t 5-3.0; df 5 25; P \u003c .01). Post hoc analyses revealed that differences in group performance were due to the shoulder group's higher scores on palpation of the biceps tendon (t 5-2.8; df 5 25; P 5 .01), a soft tissue landmark. Both groups performed similarly on palpation of all other anatomic structures. Conclusions-The use of ultrasound appears to provide an educational advantage when learning musculoskeletal physical examination of soft tissue landmarks.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Journal of Ultrasound in Medicine"},"translated_abstract":"Objectives-Ultrasound imaging is commonly used to teach basic anatomy to medical students. The purpose of this study was to determine whether learning musculo-skeletal anatomy with ultrasound improved performance on medical students' musculoskeletal physical examination skills. Methods-Twenty-seven first-year medical students were randomly assigned to 1 of 2 instructional groups: either shoulder or knee. Both groups received a lecture followed by hands-on ultrasound scanning on live human models of the assigned joint. After instruction, students were assessed on their ability to accurately palpate 4 ana-tomic landmarks: the acromioclavicular joint, the proximal long-head biceps tendon, and the medial and lateral joint lines of the knee. Performance scores were based on both accuracy and time. A total physical examination performance score was derived for each joint. Scores for instructional groups were compared by a 2-way analysis of variance with 1 repeated measure. Significant findings were further analyzed with post hoc tests. Results-All students performed significantly better on the knee examination, irrespective of instructional group (F 5 14.9; df 5 1.25; P 5 .001). Moreover, the shoulder instruction group performed significantly better than the knee group on the overall assessment (t 5-3.0; df 5 25; P \u003c .01). Post hoc analyses revealed that differences in group performance were due to the shoulder group's higher scores on palpation of the biceps tendon (t 5-2.8; df 5 25; P 5 .01), a soft tissue landmark. Both groups performed similarly on palpation of all other anatomic structures. Conclusions-The use of ultrasound appears to provide an educational advantage when learning musculoskeletal physical examination of soft tissue landmarks.","internal_url":"https://www.academia.edu/40049306/Does_Ultrasound_Enhanced_Instruction_of_Musculoskeletal_Anatomy_Improve_Physical_Examination_Skills_of_First_Year_Medical_Students","translated_internal_url":"","created_at":"2019-08-09T15:09:43.443-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892991,"work_id":40049306,"tagging_user_id":7626683,"tagged_user_id":122763331,"co_author_invite_id":6885052,"email":"a***1@alumni.nd.edu","display_order":1,"name":"Allison Schroeder","title":"Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical Students"},{"id":32892992,"work_id":40049306,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":2,"name":"Marcia Bockbrader","title":"Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical Students"},{"id":32892993,"work_id":40049306,"tagging_user_id":7626683,"tagged_user_id":38450590,"co_author_invite_id":null,"email":"d***y@osumc.edu","affiliation":"The Ohio State University","display_order":3,"name":"David Way","title":"Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical Students"},{"id":32892994,"work_id":40049306,"tagging_user_id":7626683,"tagged_user_id":38233222,"co_author_invite_id":null,"email":"d***r@osumc.edu","display_order":4,"name":"David Bahner","title":"Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical Students"}],"downloadable_attachments":[{"id":60248785,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248785/thumbnails/1.jpg","file_name":"Walrod_et_al-2017-Journal_of_Ultrasound_in_Medicine20190809-73618-1jlexqj.pdf","download_url":"https://www.academia.edu/attachments/60248785/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Does_Ultrasound_Enhanced_Instruction_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248785/Walrod_et_al-2017-Journal_of_Ultrasound_in_Medicine20190809-73618-1jlexqj-libre.pdf?1565390548=\u0026response-content-disposition=attachment%3B+filename%3DDoes_Ultrasound_Enhanced_Instruction_of.pdf\u0026Expires=1732437019\u0026Signature=Z79fPS1qdFaPlZF~39K79r-CJ0eqfvPHfaJ~wdaAsRjrDpk~QEU7GG~ZzaZ11l46e0vEDJrQ8jE5g2-Vb7Cpd3vxtNA-A-6FUMJaA8IZ2bG~MO7XL2pH2Kq0Mnh7oNR4iQlTXjLQqJa~52ga-h4vmSITrPXOCBSgn~Kvj6HqbUF5d7aFu568KjUw2FkQRs80jrcm72gJCmWO9YjpO23S~vV3mVSoG030s5-6~mqf0Ly3~pM7jM352cVWseI5H9SszlJN9bW~EzgOCQUrSd6sGMs3yYEKs6Vfcg0kbbaMiWCMxUCMn-BJnny8lfxkEwRl2UTcgd74zUUviqM36uvE5w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Does_Ultrasound_Enhanced_Instruction_of_Musculoskeletal_Anatomy_Improve_Physical_Examination_Skills_of_First_Year_Medical_Students","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248785,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248785/thumbnails/1.jpg","file_name":"Walrod_et_al-2017-Journal_of_Ultrasound_in_Medicine20190809-73618-1jlexqj.pdf","download_url":"https://www.academia.edu/attachments/60248785/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Does_Ultrasound_Enhanced_Instruction_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248785/Walrod_et_al-2017-Journal_of_Ultrasound_in_Medicine20190809-73618-1jlexqj-libre.pdf?1565390548=\u0026response-content-disposition=attachment%3B+filename%3DDoes_Ultrasound_Enhanced_Instruction_of.pdf\u0026Expires=1732437019\u0026Signature=Z79fPS1qdFaPlZF~39K79r-CJ0eqfvPHfaJ~wdaAsRjrDpk~QEU7GG~ZzaZ11l46e0vEDJrQ8jE5g2-Vb7Cpd3vxtNA-A-6FUMJaA8IZ2bG~MO7XL2pH2Kq0Mnh7oNR4iQlTXjLQqJa~52ga-h4vmSITrPXOCBSgn~Kvj6HqbUF5d7aFu568KjUw2FkQRs80jrcm72gJCmWO9YjpO23S~vV3mVSoG030s5-6~mqf0Ly3~pM7jM352cVWseI5H9SszlJN9bW~EzgOCQUrSd6sGMs3yYEKs6Vfcg0kbbaMiWCMxUCMn-BJnny8lfxkEwRl2UTcgd74zUUviqM36uvE5w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":4455,"name":"Medical Education","url":"https://www.academia.edu/Documents/in/Medical_Education"},{"id":119911,"name":"Musculoskeletal Ultrasound","url":"https://www.academia.edu/Documents/in/Musculoskeletal_Ultrasound"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049300"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049300/Dexterous_Control_of_Seven_Functional_Hand_Movements_Using_Cortically_Controlled_Transcutaneous_Muscle_Stimulation_in_a_Person_With_Tetraplegia"><img alt="Research paper thumbnail of Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia" class="work-thumbnail" src="https://attachments.academia-assets.com/60248778/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049300/Dexterous_Control_of_Seven_Functional_Hand_Movements_Using_Cortically_Controlled_Transcutaneous_Muscle_Stimulation_in_a_Person_With_Tetraplegia">Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia</a></div><div class="wp-workCard_item"><span>Frontiers in Neuroscience </span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to r...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with >95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="11d4a683243a043e6b4581de6f4f930f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248778,"asset_id":40049300,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248778/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049300"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049300"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049300; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049300]").text(description); $(".js-view-count[data-work-id=40049300]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049300; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049300']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049300, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "11d4a683243a043e6b4581de6f4f930f" } } $('.js-work-strip[data-work-id=40049300]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049300,"title":"Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia","translated_title":"","metadata":{"doi":"10.3389/fnins.2018.00208","abstract":"Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with \u003e95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Frontiers in Neuroscience "},"translated_abstract":"Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with \u003e95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.","internal_url":"https://www.academia.edu/40049300/Dexterous_Control_of_Seven_Functional_Hand_Movements_Using_Cortically_Controlled_Transcutaneous_Muscle_Stimulation_in_a_Person_With_Tetraplegia","translated_internal_url":"","created_at":"2019-08-09T15:07:46.540-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892986,"work_id":40049300,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":6885050,"email":"s***g@battelle.org","display_order":1,"name":"Gaurav Sharma","title":"Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia"},{"id":32892987,"work_id":40049300,"tagging_user_id":7626683,"tagged_user_id":43069448,"co_author_invite_id":null,"email":"s***s@osumc.edu","display_order":2,"name":"Sam Colachis","title":"Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia"},{"id":32892988,"work_id":40049300,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":6327072,"email":"b***3@osu.edu","display_order":3,"name":"Marcie Bockbrader","title":"Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia"},{"id":32892989,"work_id":40049300,"tagging_user_id":7626683,"tagged_user_id":47636515,"co_author_invite_id":null,"email":"f***d@battelle.org","display_order":4,"name":"David Friedenberg","title":"Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia"},{"id":32892990,"work_id":40049300,"tagging_user_id":7626683,"tagged_user_id":38974648,"co_author_invite_id":null,"email":"w***w@osumc.edu","display_order":5,"name":"W. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049275"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049275/Extracting_wavelet_based_neural_features_from_human_intracortical_recordings_for_neuroprosthetics_applications"><img alt="Research paper thumbnail of Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications" class="work-thumbnail" src="https://attachments.academia-assets.com/60248750/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049275/Extracting_wavelet_based_neural_features_from_human_intracortical_recordings_for_neuroprosthetics_applications">Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications</a></div><div class="wp-workCard_item"><span>Bioelectronic Medicine</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background: Understanding the long-term behavior of intracortically-recorded signals is essential...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain.<br />Methods: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate<br />the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor<br />cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean<br />wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP<br />(lf-MWP, 0–234 Hz), mid-frequency MWP (mf-MWP, 234 Hz–3.75 kHz) and high-frequency MWP (hf-MWP, >3.75 kHz).<br />We analyzed these features using data collected from two experiments that were repeated over the course of about<br />3 years and compared their signal stability and decoding performance with the more standard threshold crossings,<br />local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings.<br />Results: All neural features could stably track neural information for over 3 years post-implantation and were less<br />prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector<br />machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively,<br />in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings.<br />Conclusions: Our results suggest that usingMWP features in the appropriate frequency bands can provide an effective<br />neural feature for brain computer interface intended for chronic applications.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c3ff2d6acad89b88c36cd0a563913def" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248750,"asset_id":40049275,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248750/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049275"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049275"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049275; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049275]").text(description); $(".js-view-count[data-work-id=40049275]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049275; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049275']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049275, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c3ff2d6acad89b88c36cd0a563913def" } } $('.js-work-strip[data-work-id=40049275]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049275,"title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications","translated_title":"","metadata":{"doi":"10.1186/s42234-018-0011-x","abstract":"Background: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain.\nMethods: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate\nthe utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor\ncortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean\nwavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP\n(lf-MWP, 0–234 Hz), mid-frequency MWP (mf-MWP, 234 Hz–3.75 kHz) and high-frequency MWP (hf-MWP, \u003e3.75 kHz).\nWe analyzed these features using data collected from two experiments that were repeated over the course of about\n3 years and compared their signal stability and decoding performance with the more standard threshold crossings,\nlocal field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings.\nResults: All neural features could stably track neural information for over 3 years post-implantation and were less\nprone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector\nmachine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively,\nin classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings.\nConclusions: Our results suggest that usingMWP features in the appropriate frequency bands can provide an effective\nneural feature for brain computer interface intended for chronic applications.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Bioelectronic Medicine"},"translated_abstract":"Background: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain.\nMethods: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate\nthe utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor\ncortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean\nwavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP\n(lf-MWP, 0–234 Hz), mid-frequency MWP (mf-MWP, 234 Hz–3.75 kHz) and high-frequency MWP (hf-MWP, \u003e3.75 kHz).\nWe analyzed these features using data collected from two experiments that were repeated over the course of about\n3 years and compared their signal stability and decoding performance with the more standard threshold crossings,\nlocal field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings.\nResults: All neural features could stably track neural information for over 3 years post-implantation and were less\nprone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector\nmachine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively,\nin classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings.\nConclusions: Our results suggest that usingMWP features in the appropriate frequency bands can provide an effective\nneural feature for brain computer interface intended for chronic applications.","internal_url":"https://www.academia.edu/40049275/Extracting_wavelet_based_neural_features_from_human_intracortical_recordings_for_neuroprosthetics_applications","translated_internal_url":"","created_at":"2019-08-09T15:02:33.916-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892980,"work_id":40049275,"tagging_user_id":7626683,"tagged_user_id":47636515,"co_author_invite_id":null,"email":"f***d@battelle.org","display_order":1,"name":"David Friedenberg","title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications"},{"id":32892981,"work_id":40049275,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":2,"name":"Marcia Bockbrader","title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications"},{"id":32892982,"work_id":40049275,"tagging_user_id":7626683,"tagged_user_id":38974648,"co_author_invite_id":null,"email":"w***w@osumc.edu","display_order":3,"name":"W. Mysiw","title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications"},{"id":32892983,"work_id":40049275,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":4404282,"email":"b***n@battelle.org","display_order":4,"name":"Chad Bouton","title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications"},{"id":32892984,"work_id":40049275,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":6885050,"email":"s***g@battelle.org","display_order":5,"name":"Gaurav Sharma","title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications"}],"downloadable_attachments":[{"id":60248750,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248750/thumbnails/1.jpg","file_name":"Zhang_etal2018_chronic20190809-73635-a0kwzo.pdf","download_url":"https://www.academia.edu/attachments/60248750/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Extracting_wavelet_based_neural_features.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248750/Zhang_etal2018_chronic20190809-73635-a0kwzo-libre.pdf?1565388924=\u0026response-content-disposition=attachment%3B+filename%3DExtracting_wavelet_based_neural_features.pdf\u0026Expires=1732225872\u0026Signature=AuzBL3NAUdxPfTTgFwepDkJmVgeY58mCdufjkZbejBxhIwskmPss9wbncwUb~HXPFwAZqjRkHrNTRBidNwrzmAY4Cb49lgYXjTRfen24ft8TtUeAhSVAwqsjKaKSm0k7Zbx9wuYrNvcLbhTYgO3YHDlNobMNmPlNgOPoH98pIHQxkyUIrzvKgfiKxeUKF4Ai~xZsgIEmgmO1Vq5tTNLK5tn319WU8IUOnWUCrSeWTwMAkGamZaxNRJgvHoY73aMFG95gkXTNr8eT-rodP-w-Ju81A2mmFl-8u3~tVCabg1U2C5Xv9acdvtuNJ2o8mbrwaEWSLQ-h0cyXaeDFPFxhjQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Extracting_wavelet_based_neural_features_from_human_intracortical_recordings_for_neuroprosthetics_applications","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248750,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248750/thumbnails/1.jpg","file_name":"Zhang_etal2018_chronic20190809-73635-a0kwzo.pdf","download_url":"https://www.academia.edu/attachments/60248750/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Extracting_wavelet_based_neural_features.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248750/Zhang_etal2018_chronic20190809-73635-a0kwzo-libre.pdf?1565388924=\u0026response-content-disposition=attachment%3B+filename%3DExtracting_wavelet_based_neural_features.pdf\u0026Expires=1732225872\u0026Signature=AuzBL3NAUdxPfTTgFwepDkJmVgeY58mCdufjkZbejBxhIwskmPss9wbncwUb~HXPFwAZqjRkHrNTRBidNwrzmAY4Cb49lgYXjTRfen24ft8TtUeAhSVAwqsjKaKSm0k7Zbx9wuYrNvcLbhTYgO3YHDlNobMNmPlNgOPoH98pIHQxkyUIrzvKgfiKxeUKF4Ai~xZsgIEmgmO1Vq5tTNLK5tn319WU8IUOnWUCrSeWTwMAkGamZaxNRJgvHoY73aMFG95gkXTNr8eT-rodP-w-Ju81A2mmFl-8u3~tVCabg1U2C5Xv9acdvtuNJ2o8mbrwaEWSLQ-h0cyXaeDFPFxhjQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":15811,"name":"Biomedical signal and image processing","url":"https://www.academia.edu/Documents/in/Biomedical_signal_and_image_processing"},{"id":91365,"name":"Wavelet Transforms","url":"https://www.academia.edu/Documents/in/Wavelet_Transforms"},{"id":384468,"name":"Bio-implantable Circuits and Neural Signal Processing","url":"https://www.academia.edu/Documents/in/Bio-implantable_Circuits_and_Neural_Signal_Processing"},{"id":644153,"name":"Brain Computer Interface BCI","url":"https://www.academia.edu/Documents/in/Brain_Computer_Interface_BCI"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049262"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049262/Meeting_brain_computer_interface_user_performance_expectations_using_a_deep_neural_network_decoding_framework"><img alt="Research paper thumbnail of Meeting brain-computer interface user performance expectations using a deep neural network decoding framework" class="work-thumbnail" src="https://attachments.academia-assets.com/60248738/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049262/Meeting_brain_computer_interface_user_performance_expectations_using_a_deep_neural_network_decoding_framework">Meeting brain-computer interface user performance expectations using a deep neural network decoding framework</a></div><div class="wp-workCard_item"><span>Nature Medicine</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices 1-9. Surveys of potential end-users have identified key BCI system features 10-14 , including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm 1,15 , which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network 16 decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracorti-cal data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure 3,17-20 , responds faster than competing methods 8 , and can increase functionality with minimal retraining by using a technique known as transfer learning 21. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT) 22. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology. The study participant was a 27-year-old male with C5 AIS A tetraplegia due to spinal cord injury. He was implanted with a 96-channel microelectrode array in the hand and arm area of his left primary motor cortex 8,23,24. We trained and evaluated BCI decoders using 80 sessions of intracortical data collected from the participant over 865 d. During each session, the participant performed two 104-s blocks of the four-movement task (Methods), in which he was cued to imagine a series of four distinct hand movements (index extension, index flexion, wrist extension, wrist flexion) in a random order (Fig. 1a,b). We calibrated the initial neural network (NN) model using 40 sessions (80 blocks) from the training period (Fig. 1c). As the model was not updated at all over the subsequent test period, we call it the fixed NN (fNN). Two additional NN models were created from the fNN using updating procedures that used the first block of each of the 40 sessions in the testing period in different ways (Fig. 1c): supervised updating (sNN) or unsupervised updating (uNN) (Fig. 1d and Methods). In this context, supervised refers to the algorithm using explicit training labels (i.e., known timing and type of intended action) as opposed to unsupervised, in which the timing and type of intended action were unknown, as occurs with general BCI use. For comparison, the first block of each of the 40 sessions in the testing period was also used to calibrate benchmark BCI decoders that were retrained daily: a support vector machine (SVM) decoder (Fig. 1d) 8,23,25,26 , a linear discriminant analysis (LDA) decoder 17 , and a naive Bayes decoder 18. The SVM performed better than the LDA or naive Bayes decoder (Supplementary Fig. 1) and was thus used for further comparisons with NN performance. Neural features used by all models were the mean wavelet power (MWP) values calculated from raw voltage for each of the 96 channels over 100-ms bins 8,23,24 (Fig. 1e, Supplementary Fig. 2, and Methods). Performance for each of the NN and comparison models was initially evaluated using accuracy (percentage of correctly predicted time-bins) on the second block of data from each session during the testing period (Methods). Figure 1e shows data processing steps and NN model architecture. To quantify improved BCI accuracy with the NN, we compared the performance of the supervised, daily-updated sNN against a daily-retrained SVM. Figure 2a,b shows that the sNN was more accurate than the daily-retrained SVM for all sessions, with a mean difference of 6.35 ± 2.47% (mean ± s.d.; P = 3.69 × 20-8 , V = 820, n = 40 paired two-sided Wilcoxon signed rank test; n is the sample size and V is the test statistic for the paired Wilcoxon test). In addition, for 37 out of 40 sessions, the sNN accuracy was > 90%, indicating consistently high performance in accordance with user expectations 13. In contrast, the SVM accuracy was > 90% for only 12 sessions. To demonstrate that a BCI with a neural network decoder (NN-BCI) could sustain high accuracy for over a year without the need of supervised updating (thus reducing daily setup time), we evaluated performance of the fNN. Figure 2c shows that the fNN was more accurate than the daily-retrained SVM for 36 out of 40 test sessions, with a mean difference of 4.56. ± 3.06% (P = 1.90 × 10-7 , V = 798, n = 40; Fig. 2c, inset). In addition, the fNN accuracy was > 90% for 32 sessions. Not only was the fNN able to sustain high accuracy decoding performance for over a year (381 d) without being recalibrated, it significantly outperformed all fixed versions of the benchmark decoders we tested (Supplementary Fig. 1). However, the fNN accuracy was lower than that of the sNN (Fig. 2d), which received supervised updates throughout the testing period. In fact,</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9c08762c780753719b8305dcc353ff87" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248738,"asset_id":40049262,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248738/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049262"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049262"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049262; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049262]").text(description); $(".js-view-count[data-work-id=40049262]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049262; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049262']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049262, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9c08762c780753719b8305dcc353ff87" } } $('.js-work-strip[data-work-id=40049262]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049262,"title":"Meeting brain-computer interface user performance expectations using a deep neural network decoding framework","translated_title":"","metadata":{"doi":"10.1038/s41591-018-0171-y","abstract":"Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices 1-9. Surveys of potential end-users have identified key BCI system features 10-14 , including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm 1,15 , which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network 16 decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracorti-cal data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure 3,17-20 , responds faster than competing methods 8 , and can increase functionality with minimal retraining by using a technique known as transfer learning 21. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT) 22. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology. The study participant was a 27-year-old male with C5 AIS A tetraplegia due to spinal cord injury. He was implanted with a 96-channel microelectrode array in the hand and arm area of his left primary motor cortex 8,23,24. We trained and evaluated BCI decoders using 80 sessions of intracortical data collected from the participant over 865 d. During each session, the participant performed two 104-s blocks of the four-movement task (Methods), in which he was cued to imagine a series of four distinct hand movements (index extension, index flexion, wrist extension, wrist flexion) in a random order (Fig. 1a,b). We calibrated the initial neural network (NN) model using 40 sessions (80 blocks) from the training period (Fig. 1c). As the model was not updated at all over the subsequent test period, we call it the fixed NN (fNN). Two additional NN models were created from the fNN using updating procedures that used the first block of each of the 40 sessions in the testing period in different ways (Fig. 1c): supervised updating (sNN) or unsupervised updating (uNN) (Fig. 1d and Methods). In this context, supervised refers to the algorithm using explicit training labels (i.e., known timing and type of intended action) as opposed to unsupervised, in which the timing and type of intended action were unknown, as occurs with general BCI use. For comparison, the first block of each of the 40 sessions in the testing period was also used to calibrate benchmark BCI decoders that were retrained daily: a support vector machine (SVM) decoder (Fig. 1d) 8,23,25,26 , a linear discriminant analysis (LDA) decoder 17 , and a naive Bayes decoder 18. The SVM performed better than the LDA or naive Bayes decoder (Supplementary Fig. 1) and was thus used for further comparisons with NN performance. Neural features used by all models were the mean wavelet power (MWP) values calculated from raw voltage for each of the 96 channels over 100-ms bins 8,23,24 (Fig. 1e, Supplementary Fig. 2, and Methods). Performance for each of the NN and comparison models was initially evaluated using accuracy (percentage of correctly predicted time-bins) on the second block of data from each session during the testing period (Methods). Figure 1e shows data processing steps and NN model architecture. To quantify improved BCI accuracy with the NN, we compared the performance of the supervised, daily-updated sNN against a daily-retrained SVM. Figure 2a,b shows that the sNN was more accurate than the daily-retrained SVM for all sessions, with a mean difference of 6.35 ± 2.47% (mean ± s.d.; P = 3.69 × 20-8 , V = 820, n = 40 paired two-sided Wilcoxon signed rank test; n is the sample size and V is the test statistic for the paired Wilcoxon test). In addition, for 37 out of 40 sessions, the sNN accuracy was \u003e 90%, indicating consistently high performance in accordance with user expectations 13. In contrast, the SVM accuracy was \u003e 90% for only 12 sessions. To demonstrate that a BCI with a neural network decoder (NN-BCI) could sustain high accuracy for over a year without the need of supervised updating (thus reducing daily setup time), we evaluated performance of the fNN. Figure 2c shows that the fNN was more accurate than the daily-retrained SVM for 36 out of 40 test sessions, with a mean difference of 4.56. ± 3.06% (P = 1.90 × 10-7 , V = 798, n = 40; Fig. 2c, inset). In addition, the fNN accuracy was \u003e 90% for 32 sessions. Not only was the fNN able to sustain high accuracy decoding performance for over a year (381 d) without being recalibrated, it significantly outperformed all fixed versions of the benchmark decoders we tested (Supplementary Fig. 1). However, the fNN accuracy was lower than that of the sNN (Fig. 2d), which received supervised updates throughout the testing period. In fact,","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Nature Medicine"},"translated_abstract":"Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices 1-9. Surveys of potential end-users have identified key BCI system features 10-14 , including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm 1,15 , which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network 16 decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracorti-cal data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure 3,17-20 , responds faster than competing methods 8 , and can increase functionality with minimal retraining by using a technique known as transfer learning 21. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT) 22. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology. The study participant was a 27-year-old male with C5 AIS A tetraplegia due to spinal cord injury. He was implanted with a 96-channel microelectrode array in the hand and arm area of his left primary motor cortex 8,23,24. We trained and evaluated BCI decoders using 80 sessions of intracortical data collected from the participant over 865 d. During each session, the participant performed two 104-s blocks of the four-movement task (Methods), in which he was cued to imagine a series of four distinct hand movements (index extension, index flexion, wrist extension, wrist flexion) in a random order (Fig. 1a,b). We calibrated the initial neural network (NN) model using 40 sessions (80 blocks) from the training period (Fig. 1c). As the model was not updated at all over the subsequent test period, we call it the fixed NN (fNN). Two additional NN models were created from the fNN using updating procedures that used the first block of each of the 40 sessions in the testing period in different ways (Fig. 1c): supervised updating (sNN) or unsupervised updating (uNN) (Fig. 1d and Methods). In this context, supervised refers to the algorithm using explicit training labels (i.e., known timing and type of intended action) as opposed to unsupervised, in which the timing and type of intended action were unknown, as occurs with general BCI use. For comparison, the first block of each of the 40 sessions in the testing period was also used to calibrate benchmark BCI decoders that were retrained daily: a support vector machine (SVM) decoder (Fig. 1d) 8,23,25,26 , a linear discriminant analysis (LDA) decoder 17 , and a naive Bayes decoder 18. The SVM performed better than the LDA or naive Bayes decoder (Supplementary Fig. 1) and was thus used for further comparisons with NN performance. Neural features used by all models were the mean wavelet power (MWP) values calculated from raw voltage for each of the 96 channels over 100-ms bins 8,23,24 (Fig. 1e, Supplementary Fig. 2, and Methods). Performance for each of the NN and comparison models was initially evaluated using accuracy (percentage of correctly predicted time-bins) on the second block of data from each session during the testing period (Methods). Figure 1e shows data processing steps and NN model architecture. To quantify improved BCI accuracy with the NN, we compared the performance of the supervised, daily-updated sNN against a daily-retrained SVM. Figure 2a,b shows that the sNN was more accurate than the daily-retrained SVM for all sessions, with a mean difference of 6.35 ± 2.47% (mean ± s.d.; P = 3.69 × 20-8 , V = 820, n = 40 paired two-sided Wilcoxon signed rank test; n is the sample size and V is the test statistic for the paired Wilcoxon test). In addition, for 37 out of 40 sessions, the sNN accuracy was \u003e 90%, indicating consistently high performance in accordance with user expectations 13. In contrast, the SVM accuracy was \u003e 90% for only 12 sessions. To demonstrate that a BCI with a neural network decoder (NN-BCI) could sustain high accuracy for over a year without the need of supervised updating (thus reducing daily setup time), we evaluated performance of the fNN. Figure 2c shows that the fNN was more accurate than the daily-retrained SVM for 36 out of 40 test sessions, with a mean difference of 4.56. ± 3.06% (P = 1.90 × 10-7 , V = 798, n = 40; Fig. 2c, inset). In addition, the fNN accuracy was \u003e 90% for 32 sessions. Not only was the fNN able to sustain high accuracy decoding performance for over a year (381 d) without being recalibrated, it significantly outperformed all fixed versions of the benchmark decoders we tested (Supplementary Fig. 1). However, the fNN accuracy was lower than that of the sNN (Fig. 2d), which received supervised updates throughout the testing period. In fact,","internal_url":"https://www.academia.edu/40049262/Meeting_brain_computer_interface_user_performance_expectations_using_a_deep_neural_network_decoding_framework","translated_internal_url":"","created_at":"2019-08-09T14:59:20.279-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892977,"work_id":40049262,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":1,"name":"Marcia Bockbrader","title":"Meeting brain-computer interface user performance expectations using a deep neural network decoding framework"},{"id":32892978,"work_id":40049262,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":6885049,"email":"s***r@battelle.org","display_order":2,"name":"Michael Schwemmer","title":"Meeting brain-computer interface user performance 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class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049219/A_Characterization_of_Brain_Computer_Interface_Performance_Trade_Offs_Using_Support_Vector_Machines_and_Deep_Neural_Networks_to_Decode_Movement_Intent">A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent</a></div><div class="wp-workCard_item"><span>Frontiers in Neuroscience</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing dis...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. Surveys of potential users have revealed several key BCI performance criteria for clinical translation of such a system. Of these criteria, high accuracy, short response latencies, and multi-functionality are three key characteristics directly impacted by the neural decoding component of the BCI system, the algorithm that translates neural activity into control signals. Building a decoder that simultaneously addresses these three criteria is complicated because optimizing for one criterion may lead to undesirable changes in the other criteria. Unfortunately, there has been little work to date to quantify how decoder design simultaneously affects these performance characteristics. Here, we systematically explore the trade-off between accuracy, response latency, and multi-functionality for discrete movement classification using two different decoding strategies-a support vector machine (SVM) classifier which represents the current state-of-the-art for discrete movement classification in laboratory demonstrations and a proposed deep neural network (DNN) framework. We utilized historical intracortical recordings from a human tetraplegic study participant, who imagined performing several different hand and finger movements. For both decoders, we found that response time increases (i.e., slower reaction) and accuracy decreases as the number of functions increases. However, we also found that both the increase of response times and the decline in accuracy with additional functions is less for the DNN than the SVM. We also show that data preprocessing steps can affect the performance characteristics of the two decoders in drastically different ways. Finally, we evaluated the performance of our tetraplegic participant using the DNN decoder in real-time to control functional electrical stimulation (FES) of his paralyzed forearm. We compared his</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7bccb694ff8c6726904719ca3d6cd1f6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248689,"asset_id":40049219,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248689/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049219"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049219"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049219; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049219]").text(description); $(".js-view-count[data-work-id=40049219]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049219; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049219']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049219, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7bccb694ff8c6726904719ca3d6cd1f6" } } $('.js-work-strip[data-work-id=40049219]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049219,"title":"A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent","translated_title":"","metadata":{"doi":"10.3389/fnins.2018.00763","volume":"12","abstract":"Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. 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Building a decoder that simultaneously addresses these three criteria is complicated because optimizing for one criterion may lead to undesirable changes in the other criteria. Unfortunately, there has been little work to date to quantify how decoder design simultaneously affects these performance characteristics. Here, we systematically explore the trade-off between accuracy, response latency, and multi-functionality for discrete movement classification using two different decoding strategies-a support vector machine (SVM) classifier which represents the current state-of-the-art for discrete movement classification in laboratory demonstrations and a proposed deep neural network (DNN) framework. We utilized historical intracortical recordings from a human tetraplegic study participant, who imagined performing several different hand and finger movements. 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We compared his","internal_url":"https://www.academia.edu/40049219/A_Characterization_of_Brain_Computer_Interface_Performance_Trade_Offs_Using_Support_Vector_Machines_and_Deep_Neural_Networks_to_Decode_Movement_Intent","translated_internal_url":"","created_at":"2019-08-09T14:47:37.753-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892942,"work_id":40049219,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":6885046,"email":"s***k@battelle.org","display_order":1,"name":"Nicholas Skomrock","title":"A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent"},{"id":32892943,"work_id":40049219,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":2,"name":"Marcia Bockbrader","title":"A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent"},{"id":32892944,"work_id":40049219,"tagging_user_id":7626683,"tagged_user_id":47636515,"co_author_invite_id":null,"email":"f***d@battelle.org","display_order":3,"name":"David Friedenberg","title":"A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent"}],"downloadable_attachments":[{"id":60248689,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248689/thumbnails/1.jpg","file_name":"Skomrock_etal2018_fnins-12-0076320190809-29369-zvfu1b.pdf","download_url":"https://www.academia.edu/attachments/60248689/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Characterization_of_Brain_Computer_Int.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248689/Skomrock_etal2018_fnins-12-0076320190809-29369-zvfu1b-libre.pdf?1565388297=\u0026response-content-disposition=attachment%3B+filename%3DA_Characterization_of_Brain_Computer_Int.pdf\u0026Expires=1732437019\u0026Signature=Z1yqxZkaCBXu-a6w0o7duLo3Lkq-6cYUINDLeO9dTrlJRCnrmWqJp5B2WwAYktXI2ijQl8DbemKnuyRtmbmrrVYMIWGoWU1nIpgu71Bu1c5H~MoXSaBRKTNvGSwqqrCyG7OW7t3Vf30iy3b624-BddL3ZHjon6MVFOouH5PHuISZB5WSWGZPcsTSYJNrRxPxSa6m-ZJeZ4YmMJDquF3w2BSIHrmOmkQYusZBXugRoq4h6QyQJKkVfSOQZHqmzgIQebAkj5SdLy~fx7j0Gb5Ga7PycACvABalunotxx3kgvUFNz~SQ6wkle1E-DeCjJPgpmro301bKyOQ~MaFW9a~TA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Characterization_of_Brain_Computer_Interface_Performance_Trade_Offs_Using_Support_Vector_Machines_and_Deep_Neural_Networks_to_Decode_Movement_Intent","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248689,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248689/thumbnails/1.jpg","file_name":"Skomrock_etal2018_fnins-12-0076320190809-29369-zvfu1b.pdf","download_url":"https://www.academia.edu/attachments/60248689/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Characterization_of_Brain_Computer_Int.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248689/Skomrock_etal2018_fnins-12-0076320190809-29369-zvfu1b-libre.pdf?1565388297=\u0026response-content-disposition=attachment%3B+filename%3DA_Characterization_of_Brain_Computer_Int.pdf\u0026Expires=1732437019\u0026Signature=Z1yqxZkaCBXu-a6w0o7duLo3Lkq-6cYUINDLeO9dTrlJRCnrmWqJp5B2WwAYktXI2ijQl8DbemKnuyRtmbmrrVYMIWGoWU1nIpgu71Bu1c5H~MoXSaBRKTNvGSwqqrCyG7OW7t3Vf30iy3b624-BddL3ZHjon6MVFOouH5PHuISZB5WSWGZPcsTSYJNrRxPxSa6m-ZJeZ4YmMJDquF3w2BSIHrmOmkQYusZBXugRoq4h6QyQJKkVfSOQZHqmzgIQebAkj5SdLy~fx7j0Gb5Ga7PycACvABalunotxx3kgvUFNz~SQ6wkle1E-DeCjJPgpmro301bKyOQ~MaFW9a~TA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":18450,"name":"Neuromuscular Control","url":"https://www.academia.edu/Documents/in/Neuromuscular_Control"},{"id":46043,"name":"Functional Electrical Stimulation","url":"https://www.academia.edu/Documents/in/Functional_Electrical_Stimulation"},{"id":644153,"name":"Brain Computer Interface BCI","url":"https://www.academia.edu/Documents/in/Brain_Computer_Interface_BCI"},{"id":890015,"name":"Deep Neural Networks","url":"https://www.academia.edu/Documents/in/Deep_Neural_Networks"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049191"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049191/Beyond_Bones_Assessing_Whether_Ultrasound_Aided_Instruction_and_Practice_Improve_Unassisted_Soft_Tissue_Palpation_Skills_of_First_Year_Medical_Students"><img alt="Research paper thumbnail of Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students" class="work-thumbnail" src="https://attachments.academia-assets.com/60248667/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049191/Beyond_Bones_Assessing_Whether_Ultrasound_Aided_Instruction_and_Practice_Improve_Unassisted_Soft_Tissue_Palpation_Skills_of_First_Year_Medical_Students">Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://osu.academia.edu/MarcieBockbrader">Marcie Bockbrader</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://osu1.academia.edu/DavidWay">David Way</a></span></div><div class="wp-workCard_item"><span>Journal of Ultrasound in Medicine</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objectives-Our purpose was to determine whether ultrasound (US)-aided instruction and practice on...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objectives-Our purpose was to determine whether ultrasound (US)-aided instruction and practice on musculoskeletal anatomy would improve first-year medical students' ability to locate and identify specific soft tissue structures by unaided palpation in the upper and lower extremities of healthy human models. Methods-This study was a randomized crossover design with 49 first-year medical students randomly assigned to 1 of 2 groups. Each group was provided expert instruction and hands-on practice using US to scan and study soft tissue structures. During session 1, group A learned the anatomy of the upper extremities , whereas group B learned the lower. Students were then tested on their proficiency in locating 4 soft tissue structures (2 upper and 2 lower extremities) through palpation of a human model. During session 2, group A learned lower extremities, and group B learned upper. At the end of session 2, students repeated the assessment. Results-After the first instructional session, neither group performed significantly better on identifying and locating the soft tissue landmarks they learned aided by US. After the second instructional session, however, scores for both groups increased approximately 20 percentage points, indicating that both groups performed significantly better on palpating and identifying both the upper and lower extremity soft tissue landmarks (Cohen d = 0.89 and 0.82, respectively). Conclusions-Time and practice viewing soft tissue structures with US assistance seems to have a "palpation-with-eyes" effect that improves students' abilities to correctly locate, palpate, and identify limb-specific soft tissue structures once the US assistance is removed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6c36a35308f929ef1c2ef5666e58f804" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248667,"asset_id":40049191,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248667/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049191"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049191"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049191; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049191]").text(description); $(".js-view-count[data-work-id=40049191]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049191; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049191']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049191, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6c36a35308f929ef1c2ef5666e58f804" } } $('.js-work-strip[data-work-id=40049191]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049191,"title":"Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students","translated_title":"","metadata":{"doi":"10.1002/jum.14894","abstract":"Objectives-Our purpose was to determine whether ultrasound (US)-aided instruction and practice on musculoskeletal anatomy would improve first-year medical students' ability to locate and identify specific soft tissue structures by unaided palpation in the upper and lower extremities of healthy human models. Methods-This study was a randomized crossover design with 49 first-year medical students randomly assigned to 1 of 2 groups. Each group was provided expert instruction and hands-on practice using US to scan and study soft tissue structures. During session 1, group A learned the anatomy of the upper extremities , whereas group B learned the lower. Students were then tested on their proficiency in locating 4 soft tissue structures (2 upper and 2 lower extremities) through palpation of a human model. During session 2, group A learned lower extremities, and group B learned upper. At the end of session 2, students repeated the assessment. Results-After the first instructional session, neither group performed significantly better on identifying and locating the soft tissue landmarks they learned aided by US. After the second instructional session, however, scores for both groups increased approximately 20 percentage points, indicating that both groups performed significantly better on palpating and identifying both the upper and lower extremity soft tissue landmarks (Cohen d = 0.89 and 0.82, respectively). Conclusions-Time and practice viewing soft tissue structures with US assistance seems to have a \"palpation-with-eyes\" effect that improves students' abilities to correctly locate, palpate, and identify limb-specific soft tissue structures once the US assistance is removed.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Journal of Ultrasound in Medicine"},"translated_abstract":"Objectives-Our purpose was to determine whether ultrasound (US)-aided instruction and practice on musculoskeletal anatomy would improve first-year medical students' ability to locate and identify specific soft tissue structures by unaided palpation in the upper and lower extremities of healthy human models. Methods-This study was a randomized crossover design with 49 first-year medical students randomly assigned to 1 of 2 groups. Each group was provided expert instruction and hands-on practice using US to scan and study soft tissue structures. During session 1, group A learned the anatomy of the upper extremities , whereas group B learned the lower. Students were then tested on their proficiency in locating 4 soft tissue structures (2 upper and 2 lower extremities) through palpation of a human model. During session 2, group A learned lower extremities, and group B learned upper. At the end of session 2, students repeated the assessment. Results-After the first instructional session, neither group performed significantly better on identifying and locating the soft tissue landmarks they learned aided by US. After the second instructional session, however, scores for both groups increased approximately 20 percentage points, indicating that both groups performed significantly better on palpating and identifying both the upper and lower extremity soft tissue landmarks (Cohen d = 0.89 and 0.82, respectively). Conclusions-Time and practice viewing soft tissue structures with US assistance seems to have a \"palpation-with-eyes\" effect that improves students' abilities to correctly locate, palpate, and identify limb-specific soft tissue structures once the US assistance is removed.","internal_url":"https://www.academia.edu/40049191/Beyond_Bones_Assessing_Whether_Ultrasound_Aided_Instruction_and_Practice_Improve_Unassisted_Soft_Tissue_Palpation_Skills_of_First_Year_Medical_Students","translated_internal_url":"","created_at":"2019-08-09T14:43:25.876-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892933,"work_id":40049191,"tagging_user_id":7626683,"tagged_user_id":38450590,"co_author_invite_id":null,"email":"d***y@osumc.edu","affiliation":"The Ohio State University","display_order":1,"name":"David Way","title":"Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students"},{"id":32892934,"work_id":40049191,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":2,"name":"Marcia Bockbrader","title":"Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students"},{"id":32892935,"work_id":40049191,"tagging_user_id":7626683,"tagged_user_id":38233222,"co_author_invite_id":null,"email":"d***r@osumc.edu","display_order":3,"name":"David Bahner","title":"Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical 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Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation" class="work-thumbnail" src="https://attachments.academia-assets.com/60248646/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049173/Clinically_Significant_Gains_in_Skillful_Grasp_Coordination_by_an_Individual_With_Tetraplegia_Using_an_Implanted_Brain_Computer_Interface_With_Forearm_Transcutaneous_Muscle_Stimulation">Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation</a></div><div class="wp-workCard_item"><span>Archives of Physical Medicine & Rehabilitation</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objective: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objective: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic.<br />Design:Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days137 to 1478.Setting:Tertiary care outpatient rehabilitation center.Participant:A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injuryInterventions:After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordi-nated forearm, wrist, and hand movements.Main Outcome Measures:Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, andPrehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functionalactivity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Inde-pendence MeasureeSelf-Report [SCIM-SR]) with and without the BCI-FES.Results:With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can,fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-Twrist and handskills. QIF-SFand SCIM-SR eating, grooming, and toileting activities were expected to improvewith home use of BCI-FES. Pincer grips and mobilitywere unaffected. BCI-FES grip skills enabled the participant to play an adapted “Battleship” game and manipulate household objects.Conclusions:Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upperlimb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologiclevel gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4dd25dffa97e090f1b897d55cc1d9e9f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248646,"asset_id":40049173,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248646/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049173"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049173"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049173; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049173]").text(description); $(".js-view-count[data-work-id=40049173]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049173; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049173']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049173, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4dd25dffa97e090f1b897d55cc1d9e9f" } } $('.js-work-strip[data-work-id=40049173]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049173,"title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation","translated_title":"","metadata":{"doi":"10.1016/j.apmr.2018.07.445","abstract":"Objective: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic.\nDesign:Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days137 to 1478.Setting:Tertiary care outpatient rehabilitation center.Participant:A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injuryInterventions:After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordi-nated forearm, wrist, and hand movements.Main Outcome Measures:Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, andPrehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functionalactivity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Inde-pendence MeasureeSelf-Report [SCIM-SR]) with and without the BCI-FES.Results:With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can,fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-Twrist and handskills. QIF-SFand SCIM-SR eating, grooming, and toileting activities were expected to improvewith home use of BCI-FES. Pincer grips and mobilitywere unaffected. BCI-FES grip skills enabled the participant to play an adapted “Battleship” game and manipulate household objects.Conclusions:Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upperlimb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologiclevel gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Archives of Physical Medicine \u0026 Rehabilitation"},"translated_abstract":"Objective: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic.\nDesign:Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days137 to 1478.Setting:Tertiary care outpatient rehabilitation center.Participant:A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injuryInterventions:After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordi-nated forearm, wrist, and hand movements.Main Outcome Measures:Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, andPrehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functionalactivity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Inde-pendence MeasureeSelf-Report [SCIM-SR]) with and without the BCI-FES.Results:With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can,fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-Twrist and handskills. QIF-SFand SCIM-SR eating, grooming, and toileting activities were expected to improvewith home use of BCI-FES. Pincer grips and mobilitywere unaffected. BCI-FES grip skills enabled the participant to play an adapted “Battleship” game and manipulate household objects.Conclusions:Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upperlimb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologiclevel gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics.","internal_url":"https://www.academia.edu/40049173/Clinically_Significant_Gains_in_Skillful_Grasp_Coordination_by_an_Individual_With_Tetraplegia_Using_an_Implanted_Brain_Computer_Interface_With_Forearm_Transcutaneous_Muscle_Stimulation","translated_internal_url":"","created_at":"2019-08-09T14:39:11.298-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892927,"work_id":40049173,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":6327072,"email":"b***3@osu.edu","display_order":1,"name":"Marcie Bockbrader","title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation"},{"id":32892928,"work_id":40049173,"tagging_user_id":7626683,"tagged_user_id":47636515,"co_author_invite_id":null,"email":"f***d@battelle.org","display_order":2,"name":"David Friedenberg","title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation"},{"id":32892929,"work_id":40049173,"tagging_user_id":7626683,"tagged_user_id":43069448,"co_author_invite_id":null,"email":"s***s@osumc.edu","display_order":3,"name":"Sam Colachis","title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation"},{"id":32892930,"work_id":40049173,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":4404282,"email":"b***n@battelle.org","display_order":4,"name":"Chad Bouton","title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation"},{"id":32892931,"work_id":40049173,"tagging_user_id":7626683,"tagged_user_id":38974648,"co_author_invite_id":null,"email":"w***w@osumc.edu","display_order":5,"name":"W. Mysiw","title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation"}],"downloadable_attachments":[{"id":60248646,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248646/thumbnails/1.jpg","file_name":"Bockbrader_etal2019_BCI_SCI_ClinicalGAIN20190809-13127-lk3orw.pdf","download_url":"https://www.academia.edu/attachments/60248646/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Clinically_Significant_Gains_in_Skillful.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248646/Bockbrader_etal2019_BCI_SCI_ClinicalGAIN20190809-13127-lk3orw-libre.pdf?1565388304=\u0026response-content-disposition=attachment%3B+filename%3DClinically_Significant_Gains_in_Skillful.pdf\u0026Expires=1732225873\u0026Signature=BCrQRnguZJMrSFXsZ9CMB1sajJwsYQIXvjbFLehsCpq~7C6GBSmqPDDKvcMUHTaJwGwCgNN3kUa6yY5RlMXqmnsdSQd3CTZJcuoHiYpIfyK0re~fVUCd01JSEyG3OpsJlGuk30YOy9c9m1N85~3ji8atcvxPn0tOlmMjBMvKRaZr8kiaZerim2YsGYH2DwJVMyuxoisfgPaD6NDz7fPKT2botjQcFy7AxkE5gOKQ71MGODFcxLaDc~cpSwKFTiO4RhnE94i6lvKkLdYGXjvN4ZvcsFUcx9e8-~M7kMhfI0cTQ4H1~cpRJS~ZyFvMcHqwub3fMmFGqXBnXMod8NhREQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Clinically_Significant_Gains_in_Skillful_Grasp_Coordination_by_an_Individual_With_Tetraplegia_Using_an_Implanted_Brain_Computer_Interface_With_Forearm_Transcutaneous_Muscle_Stimulation","translated_slug":"","page_count":17,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248646,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248646/thumbnails/1.jpg","file_name":"Bockbrader_etal2019_BCI_SCI_ClinicalGAIN20190809-13127-lk3orw.pdf","download_url":"https://www.academia.edu/attachments/60248646/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Clinically_Significant_Gains_in_Skillful.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248646/Bockbrader_etal2019_BCI_SCI_ClinicalGAIN20190809-13127-lk3orw-libre.pdf?1565388304=\u0026response-content-disposition=attachment%3B+filename%3DClinically_Significant_Gains_in_Skillful.pdf\u0026Expires=1732225873\u0026Signature=BCrQRnguZJMrSFXsZ9CMB1sajJwsYQIXvjbFLehsCpq~7C6GBSmqPDDKvcMUHTaJwGwCgNN3kUa6yY5RlMXqmnsdSQd3CTZJcuoHiYpIfyK0re~fVUCd01JSEyG3OpsJlGuk30YOy9c9m1N85~3ji8atcvxPn0tOlmMjBMvKRaZr8kiaZerim2YsGYH2DwJVMyuxoisfgPaD6NDz7fPKT2botjQcFy7AxkE5gOKQ71MGODFcxLaDc~cpSwKFTiO4RhnE94i6lvKkLdYGXjvN4ZvcsFUcx9e8-~M7kMhfI0cTQ4H1~cpRJS~ZyFvMcHqwub3fMmFGqXBnXMod8NhREQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":18450,"name":"Neuromuscular Control","url":"https://www.academia.edu/Documents/in/Neuromuscular_Control"},{"id":22824,"name":"Spinal Cord Injury","url":"https://www.academia.edu/Documents/in/Spinal_Cord_Injury"},{"id":46043,"name":"Functional Electrical Stimulation","url":"https://www.academia.edu/Documents/in/Functional_Electrical_Stimulation"},{"id":644153,"name":"Brain Computer Interface BCI","url":"https://www.academia.edu/Documents/in/Brain_Computer_Interface_BCI"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049137"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049137/Towards_a_consensus_for_musculoskeletal_ultrasonography_education_in_Physical_Medicine_and_Rehabilitation_A_national_poll_of_residency_directors"><img alt="Research paper thumbnail of Towards a consensus for musculoskeletal ultrasonography education in Physical Medicine & Rehabilitation: A national poll of residency directors" class="work-thumbnail" src="https://attachments.academia-assets.com/60248606/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049137/Towards_a_consensus_for_musculoskeletal_ultrasonography_education_in_Physical_Medicine_and_Rehabilitation_A_national_poll_of_residency_directors">Towards a consensus for musculoskeletal ultrasonography education in Physical Medicine & Rehabilitation: A national poll of residency directors</a></div><div class="wp-workCard_item"><span>American Journal of Physical Medicine & Rehabilitation</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objectives The aims of the study were to evaluate integration of musculoskeletal ultrasonography ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objectives The aims of the study were to evaluate integration of musculoskeletal ultrasonography education in physical medicine and rehabilitation training programs in 2014–2015, when the American Academy of Physical Medicine & Rehabilitation and Accreditation Council for Graduate Medical Education Residency Review Committee both recognized it as a fundamental component of physiatric practice, to identify common musculoskeletal ultrasonography components of physical medicine and rehabilitation residency curricula, and to identify common barriers to integration.<br /><br />Design Survey of 78 Accreditation Council for Graduate Medical Education–accredited physical medicine and rehabilitation residency programs was conducted.<br /><br />Results The 2015 survey response rate was more than 50%, and respondents were representative of programs across the United States. Most programs (80%) reported teaching musculoskeletal ultrasonography, whereas a minority (20%) required mastery of ultrasonography skills for graduation. Ultrasonography curricula varied, although most programs agreed that the scope of resident training in physical medicine and rehabilitation should include diagnostic and interventional musculoskeletal ultrasonography, especially for key joints (shoulder, elbow, knee, wrist, hip, and ankle) and nerves (median, ulnar, fibular, tibial, radial, and sciatic). Barriers to teaching included insufficient expertise of instructors, poor access to equipment, and lack of a structured curriculum.<br /><br />Conclusions Musculoskeletal ultrasonography has become a required component of physical medicine and rehabilitation residency training. Based on survey responses and expert recommendations, we propose a structure for musculoskeletal ultrasonography curricular standards and milestones for trainee competency.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bc223b79dc35812a5ac076250796e1cf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248606,"asset_id":40049137,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248606/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049137"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049137"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049137; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049137]").text(description); $(".js-view-count[data-work-id=40049137]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049137; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049137']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049137, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "bc223b79dc35812a5ac076250796e1cf" } } $('.js-work-strip[data-work-id=40049137]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049137,"title":"Towards a consensus for musculoskeletal ultrasonography education in Physical Medicine \u0026 Rehabilitation: A national poll of residency directors","translated_title":"","metadata":{"doi":"10.1097/PHM.0000000000001195","abstract":"Objectives The aims of the study were to evaluate integration of musculoskeletal ultrasonography education in physical medicine and rehabilitation training programs in 2014–2015, when the American Academy of Physical Medicine \u0026 Rehabilitation and Accreditation Council for Graduate Medical Education Residency Review Committee both recognized it as a fundamental component of physiatric practice, to identify common musculoskeletal ultrasonography components of physical medicine and rehabilitation residency curricula, and to identify common barriers to integration.\n\nDesign Survey of 78 Accreditation Council for Graduate Medical Education–accredited physical medicine and rehabilitation residency programs was conducted.\n\nResults The 2015 survey response rate was more than 50%, and respondents were representative of programs across the United States. Most programs (80%) reported teaching musculoskeletal ultrasonography, whereas a minority (20%) required mastery of ultrasonography skills for graduation. Ultrasonography curricula varied, although most programs agreed that the scope of resident training in physical medicine and rehabilitation should include diagnostic and interventional musculoskeletal ultrasonography, especially for key joints (shoulder, elbow, knee, wrist, hip, and ankle) and nerves (median, ulnar, fibular, tibial, radial, and sciatic). Barriers to teaching included insufficient expertise of instructors, poor access to equipment, and lack of a structured curriculum.\n\nConclusions Musculoskeletal ultrasonography has become a required component of physical medicine and rehabilitation residency training. Based on survey responses and expert recommendations, we propose a structure for musculoskeletal ultrasonography curricular standards and milestones for trainee competency.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"American Journal of Physical Medicine \u0026 Rehabilitation"},"translated_abstract":"Objectives The aims of the study were to evaluate integration of musculoskeletal ultrasonography education in physical medicine and rehabilitation training programs in 2014–2015, when the American Academy of Physical Medicine \u0026 Rehabilitation and Accreditation Council for Graduate Medical Education Residency Review Committee both recognized it as a fundamental component of physiatric practice, to identify common musculoskeletal ultrasonography components of physical medicine and rehabilitation residency curricula, and to identify common barriers to integration.\n\nDesign Survey of 78 Accreditation Council for Graduate Medical Education–accredited physical medicine and rehabilitation residency programs was conducted.\n\nResults The 2015 survey response rate was more than 50%, and respondents were representative of programs across the United States. Most programs (80%) reported teaching musculoskeletal ultrasonography, whereas a minority (20%) required mastery of ultrasonography skills for graduation. Ultrasonography curricula varied, although most programs agreed that the scope of resident training in physical medicine and rehabilitation should include diagnostic and interventional musculoskeletal ultrasonography, especially for key joints (shoulder, elbow, knee, wrist, hip, and ankle) and nerves (median, ulnar, fibular, tibial, radial, and sciatic). Barriers to teaching included insufficient expertise of instructors, poor access to equipment, and lack of a structured curriculum.\n\nConclusions Musculoskeletal ultrasonography has become a required component of physical medicine and rehabilitation residency training. Based on survey responses and expert recommendations, we propose a structure for musculoskeletal ultrasonography curricular standards and milestones for trainee competency.","internal_url":"https://www.academia.edu/40049137/Towards_a_consensus_for_musculoskeletal_ultrasonography_education_in_Physical_Medicine_and_Rehabilitation_A_national_poll_of_residency_directors","translated_internal_url":"","created_at":"2019-08-09T14:26:59.320-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":60248606,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248606/thumbnails/1.jpg","file_name":"Bockbrader_etal2018_MSKUS_PMR20190809-12728-lq0rgm.pdf","download_url":"https://www.academia.edu/attachments/60248606/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Towards_a_consensus_for_musculoskeletal.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248606/Bockbrader_etal2018_MSKUS_PMR20190809-12728-lq0rgm-libre.pdf?1565388314=\u0026response-content-disposition=attachment%3B+filename%3DTowards_a_consensus_for_musculoskeletal.pdf\u0026Expires=1732437019\u0026Signature=PfDi56ZJ6ANbgVPkTbUbiJgpPNJkg3TI492JTPXkop72j6XGRwu1BnLJ1A7z~1Se8zI99x6FOsTo552qKeNn87lM-zuI7X6tnN8SgbZZWuom0g78DOPfsuPLFImXYrG08dyxrq5nI1W5~QDFnFG7k3Y5byzgHF8a7t32HNkQ3LolDEI7H77x4EO2R8~U~Z9zVwXUW0kEzo37fb4zreKqKQ00Q6Vx2G3iAzYI2cJPnDcJcBjNbEqFPlXvpIRzXQegbn2x0hV8-JXlIoJVinQwVu6YSBNWFg-aFeBKnxBmVWOKOP7-nGlGHvK7lkCQMOIqQSr9M~mZWWzMevZbHsct-w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Towards_a_consensus_for_musculoskeletal_ultrasonography_education_in_Physical_Medicine_and_Rehabilitation_A_national_poll_of_residency_directors","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248606,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248606/thumbnails/1.jpg","file_name":"Bockbrader_etal2018_MSKUS_PMR20190809-12728-lq0rgm.pdf","download_url":"https://www.academia.edu/attachments/60248606/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Towards_a_consensus_for_musculoskeletal.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248606/Bockbrader_etal2018_MSKUS_PMR20190809-12728-lq0rgm-libre.pdf?1565388314=\u0026response-content-disposition=attachment%3B+filename%3DTowards_a_consensus_for_musculoskeletal.pdf\u0026Expires=1732437019\u0026Signature=PfDi56ZJ6ANbgVPkTbUbiJgpPNJkg3TI492JTPXkop72j6XGRwu1BnLJ1A7z~1Se8zI99x6FOsTo552qKeNn87lM-zuI7X6tnN8SgbZZWuom0g78DOPfsuPLFImXYrG08dyxrq5nI1W5~QDFnFG7k3Y5byzgHF8a7t32HNkQ3LolDEI7H77x4EO2R8~U~Z9zVwXUW0kEzo37fb4zreKqKQ00Q6Vx2G3iAzYI2cJPnDcJcBjNbEqFPlXvpIRzXQegbn2x0hV8-JXlIoJVinQwVu6YSBNWFg-aFeBKnxBmVWOKOP7-nGlGHvK7lkCQMOIqQSr9M~mZWWzMevZbHsct-w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":4455,"name":"Medical Education","url":"https://www.academia.edu/Documents/in/Medical_Education"},{"id":32001,"name":"Physical Medicine and Rehabilitation","url":"https://www.academia.edu/Documents/in/Physical_Medicine_and_Rehabilitation"},{"id":119911,"name":"Musculoskeletal Ultrasound","url":"https://www.academia.edu/Documents/in/Musculoskeletal_Ultrasound"},{"id":163322,"name":"Graduate medical education","url":"https://www.academia.edu/Documents/in/Graduate_medical_education"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049105"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049105/A_High_Definition_Non_invasive_Neuromuscular_Electrical_Stimulation_System_for_Cortical_Control_of_Combinatorial_Rotary_Hand_Movements_in_a_Human_with_Tetraplegia"><img alt="Research paper thumbnail of A High Definition Non-invasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human with Tetraplegia" class="work-thumbnail" src="https://attachments.academia-assets.com/60248573/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049105/A_High_Definition_Non_invasive_Neuromuscular_Electrical_Stimulation_System_for_Cortical_Control_of_Combinatorial_Rotary_Hand_Movements_in_a_Human_with_Tetraplegia">A High Definition Non-invasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human with Tetraplegia</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Biomedical Engineering</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objective: Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on mul...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objective: Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on multiple arm and hand motor functions. Rotary hand movements, such as supination and pronation, are commonly impaired by upper extremity paralysis, and are essential for many activities of daily living. In this proof-of-concept study, we utilize a neural bypass system (NBS) to decode motor intention from motor cortex to control combinatorial rotary hand movements elicited through stimulation of the arm muscles, effectively bypassing the SCI of the study participant. We describe the NBS system architecture and design that enabled this functionality. Methods: The NBS consists of three main functional components: 1) implanted intracortical microelectrode array, 2) neural data processing using a computer, and, 3) a non-invasive neuromuscular electrical stimulation (NMES) system. Results: We address previous limitations of the NBS, and confirm the enhanced capability of the NBS to enable, in real-time, combinatorial hand rotary motor functions during a functionally relevant object manipulation task. Conclusion: This enhanced capability was enabled by accurate decoding of multiple movement intentions from the participant's motor cortex, interleaving NMES patterns to combine hand movements, and dynamically switching between NMES patterns to adjust for hand position changes during movement. Significance: These results have implications for enabling complex rotary hand functions in sequence with other</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a40b3cce90d15d2ee4a4f02cb9e614cc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248573,"asset_id":40049105,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248573/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049105"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049105"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049105; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049105]").text(description); $(".js-view-count[data-work-id=40049105]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049105; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049105']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049105, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a40b3cce90d15d2ee4a4f02cb9e614cc" } } $('.js-work-strip[data-work-id=40049105]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049105,"title":"A High Definition Non-invasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human with Tetraplegia","translated_title":"","metadata":{"doi":"10.1109/TBME.2018.2864104","abstract":"Objective: Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on multiple arm and hand motor functions. Rotary hand movements, such as supination and pronation, are commonly impaired by upper extremity paralysis, and are essential for many activities of daily living. In this proof-of-concept study, we utilize a neural bypass system (NBS) to decode motor intention from motor cortex to control combinatorial rotary hand movements elicited through stimulation of the arm muscles, effectively bypassing the SCI of the study participant. We describe the NBS system architecture and design that enabled this functionality. Methods: The NBS consists of three main functional components: 1) implanted intracortical microelectrode array, 2) neural data processing using a computer, and, 3) a non-invasive neuromuscular electrical stimulation (NMES) system. Results: We address previous limitations of the NBS, and confirm the enhanced capability of the NBS to enable, in real-time, combinatorial hand rotary motor functions during a functionally relevant object manipulation task. Conclusion: This enhanced capability was enabled by accurate decoding of multiple movement intentions from the participant's motor cortex, interleaving NMES patterns to combine hand movements, and dynamically switching between NMES patterns to adjust for hand position changes during movement. Significance: These results have implications for enabling complex rotary hand functions in sequence with other","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"IEEE Transactions on Biomedical Engineering"},"translated_abstract":"Objective: Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on multiple arm and hand motor functions. Rotary hand movements, such as supination and pronation, are commonly impaired by upper extremity paralysis, and are essential for many activities of daily living. In this proof-of-concept study, we utilize a neural bypass system (NBS) to decode motor intention from motor cortex to control combinatorial rotary hand movements elicited through stimulation of the arm muscles, effectively bypassing the SCI of the study participant. We describe the NBS system architecture and design that enabled this functionality. Methods: The NBS consists of three main functional components: 1) implanted intracortical microelectrode array, 2) neural data processing using a computer, and, 3) a non-invasive neuromuscular electrical stimulation (NMES) system. Results: We address previous limitations of the NBS, and confirm the enhanced capability of the NBS to enable, in real-time, combinatorial hand rotary motor functions during a functionally relevant object manipulation task. Conclusion: This enhanced capability was enabled by accurate decoding of multiple movement intentions from the participant's motor cortex, interleaving NMES patterns to combine hand movements, and dynamically switching between NMES patterns to adjust for hand position changes during movement. Significance: These results have implications for enabling complex rotary hand functions in sequence with other","internal_url":"https://www.academia.edu/40049105/A_High_Definition_Non_invasive_Neuromuscular_Electrical_Stimulation_System_for_Cortical_Control_of_Combinatorial_Rotary_Hand_Movements_in_a_Human_with_Tetraplegia","translated_internal_url":"","created_at":"2019-08-09T14:19:16.239-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892908,"work_id":40049105,"tagging_user_id":7626683,"tagged_user_id":47841492,"co_author_invite_id":null,"email":"a***n@battelle.org","display_order":1,"name":"Nicholas Annetta","title":"A High Definition Non-invasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human with Tetraplegia"}],"downloadable_attachments":[{"id":60248573,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248573/thumbnails/1.jpg","file_name":"Annetta_etal2018_BCIFES_rotary20190809-26013-24xrud.pdf","download_url":"https://www.academia.edu/attachments/60248573/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_High_Definition_Non_invasive_Neuromusc.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248573/Annetta_etal2018_BCIFES_rotary20190809-26013-24xrud-libre.pdf?1565388315=\u0026response-content-disposition=attachment%3B+filename%3DA_High_Definition_Non_invasive_Neuromusc.pdf\u0026Expires=1732437019\u0026Signature=GelTQCPUIQiAFZOgmV9Ap4q0hLeGwer61pdslJQr4aJCRvBbbn3M8NyytH6RsK5j9tv8GkKfReRPtfGqcGGC5BV3pr5mnkqTKbMI870cwZfCuXe1R4ncm4VaZQZ-Ibp12C0rzSqcuKmOUIQ~YG-a6qM24AMibjM1HF5kbV0kMqrIj3Uj9XukKYIJVMNMHLHTNVqnBIm1dyna5a7jsc5Nd2DkGzoxoHkCg8CorlYXzHFN2g8orJHU9FT1gdiVCSLSHXzbEIpMO1UIhgh2Ovd9Rm1HO5AChpfIrGnSe~4y444vuWhpt~48BOV46tFx7Dpn8SVXQeUeTWImnvLEpXAbOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_High_Definition_Non_invasive_Neuromuscular_Electrical_Stimulation_System_for_Cortical_Control_of_Combinatorial_Rotary_Hand_Movements_in_a_Human_with_Tetraplegia","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248573,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248573/thumbnails/1.jpg","file_name":"Annetta_etal2018_BCIFES_rotary20190809-26013-24xrud.pdf","download_url":"https://www.academia.edu/attachments/60248573/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_High_Definition_Non_invasive_Neuromusc.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248573/Annetta_etal2018_BCIFES_rotary20190809-26013-24xrud-libre.pdf?1565388315=\u0026response-content-disposition=attachment%3B+filename%3DA_High_Definition_Non_invasive_Neuromusc.pdf\u0026Expires=1732437019\u0026Signature=GelTQCPUIQiAFZOgmV9Ap4q0hLeGwer61pdslJQr4aJCRvBbbn3M8NyytH6RsK5j9tv8GkKfReRPtfGqcGGC5BV3pr5mnkqTKbMI870cwZfCuXe1R4ncm4VaZQZ-Ibp12C0rzSqcuKmOUIQ~YG-a6qM24AMibjM1HF5kbV0kMqrIj3Uj9XukKYIJVMNMHLHTNVqnBIm1dyna5a7jsc5Nd2DkGzoxoHkCg8CorlYXzHFN2g8orJHU9FT1gdiVCSLSHXzbEIpMO1UIhgh2Ovd9Rm1HO5AChpfIrGnSe~4y444vuWhpt~48BOV46tFx7Dpn8SVXQeUeTWImnvLEpXAbOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":18450,"name":"Neuromuscular Control","url":"https://www.academia.edu/Documents/in/Neuromuscular_Control"},{"id":22824,"name":"Spinal Cord Injury","url":"https://www.academia.edu/Documents/in/Spinal_Cord_Injury"},{"id":46043,"name":"Functional Electrical Stimulation","url":"https://www.academia.edu/Documents/in/Functional_Electrical_Stimulation"},{"id":644153,"name":"Brain Computer Interface BCI","url":"https://www.academia.edu/Documents/in/Brain_Computer_Interface_BCI"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="39401710"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/39401710/Randomized_Sham_Controlled_Trial_of_Navigated_Repetitive_Transcranial_Magnetic_Stimulation_for_Motor_Recovery_in_Stroke_The_NICHE_Trial"><img alt="Research paper thumbnail of Randomized Sham-Controlled Trial of Navigated Repetitive Transcranial Magnetic Stimulation for Motor Recovery in Stroke: The NICHE Trial" class="work-thumbnail" src="https://attachments.academia-assets.com/59545749/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/39401710/Randomized_Sham_Controlled_Trial_of_Navigated_Repetitive_Transcranial_Magnetic_Stimulation_for_Motor_Recovery_in_Stroke_The_NICHE_Trial">Randomized Sham-Controlled Trial of Navigated Repetitive Transcranial Magnetic Stimulation for Motor Recovery in Stroke: The NICHE Trial</a></div><div class="wp-workCard_item"><span>Stroke</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background and Purpose―We aimed to determine whether low-frequency electric field navigated repet...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background and Purpose―We aimed to determine whether low-frequency electric field navigated repetitive transcranial<br />magnetic stimulation to noninjured motor cortex versus sham repetitive transcranial magnetic stimulation avoiding motor cortex could improve arm motor function in hemiplegic stroke patients when combined with motor training.<br />Methods―Twelve outpatient US rehabilitation centers enrolled participants between May 2014 and December 2015. We delivered 1 Hz active or sham repetitive transcranial magnetic stimulation to noninjured motor cortex before each of eighteen 60-minute therapy sessions over a 6-week period, with outcomes measured at 1 week and 1, 3, and 6 months after end of treatment. The primary end point was the percentage of participants improving ≥5 points on upper extremity Fugl-Meyer score 6 months after end of treatment. Secondary analyses assessed changes on the upper extremity Fugl-Meyer and Action Research Arm Test and Wolf Motor Function Test and safety.<br />Results―Of 199 participants, 167 completed treatment and follow-up because of early discontinuation of data collection. Upper extremity Fugl-Meyer gains were significant for experimental (P<0.001) and sham groups (P<0.001). Sixty-seven percent of the experimental group (95% CI, 58%–75%) and 65% of sham group (95% CI, 52%–76%) improved ≥5 points on 6-month upper extremity Fugl-Meyer (P=0.76). There was also no difference between experimental and sham groups in the Action Research Arm Test (P=0.80) or the Wolf Motor Function Test (P=0.55). A total of 26 serious adverse events occurred in 18 participants, with none related to the study or device, and with no difference between groups.<br />Conclusions―Among patients 3 to 12 months poststroke, goal-oriented motor rehabilitation improved motor function 6<br />months after end of treatment. There was no difference between the active and sham repetitive transcranial magnetic stimulation trial arms.<br />Clinical Trial Registration―URL: <a href="https://www.clinicaltrials.gov" rel="nofollow">https://www.clinicaltrials.gov</a>. Unique identifier: NCT02089464.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="62e99481b2ff4a3b5d0ee8a2f840dd4f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":59545749,"asset_id":39401710,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/59545749/download_file?st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="39401710"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="39401710"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 39401710; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=39401710]").text(description); $(".js-view-count[data-work-id=39401710]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 39401710; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='39401710']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 39401710, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "62e99481b2ff4a3b5d0ee8a2f840dd4f" } } $('.js-work-strip[data-work-id=39401710]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":39401710,"title":"Randomized Sham-Controlled Trial of Navigated Repetitive Transcranial Magnetic Stimulation for Motor Recovery in Stroke: The NICHE Trial","translated_title":"","metadata":{"doi":"10.1161/STROKEAHA.117.020607","issue":"9","volume":"49","abstract":"Background and Purpose―We aimed to determine whether low-frequency electric field navigated repetitive transcranial\nmagnetic stimulation to noninjured motor cortex versus sham repetitive transcranial magnetic stimulation avoiding motor cortex could improve arm motor function in hemiplegic stroke patients when combined with motor training.\nMethods―Twelve outpatient US rehabilitation centers enrolled participants between May 2014 and December 2015. We delivered 1 Hz active or sham repetitive transcranial magnetic stimulation to noninjured motor cortex before each of eighteen 60-minute therapy sessions over a 6-week period, with outcomes measured at 1 week and 1, 3, and 6 months after end of treatment. The primary end point was the percentage of participants improving ≥5 points on upper extremity Fugl-Meyer score 6 months after end of treatment. Secondary analyses assessed changes on the upper extremity Fugl-Meyer and Action Research Arm Test and Wolf Motor Function Test and safety.\nResults―Of 199 participants, 167 completed treatment and follow-up because of early discontinuation of data collection. Upper extremity Fugl-Meyer gains were significant for experimental (P\u003c0.001) and sham groups (P\u003c0.001). Sixty-seven percent of the experimental group (95% CI, 58%–75%) and 65% of sham group (95% CI, 52%–76%) improved ≥5 points on 6-month upper extremity Fugl-Meyer (P=0.76). There was also no difference between experimental and sham groups in the Action Research Arm Test (P=0.80) or the Wolf Motor Function Test (P=0.55). A total of 26 serious adverse events occurred in 18 participants, with none related to the study or device, and with no difference between groups.\nConclusions―Among patients 3 to 12 months poststroke, goal-oriented motor rehabilitation improved motor function 6\nmonths after end of treatment. There was no difference between the active and sham repetitive transcranial magnetic stimulation trial arms.\nClinical Trial Registration―URL: https://www.clinicaltrials.gov. Unique identifier: NCT02089464.","page_numbers":"2138-46","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Stroke"},"translated_abstract":"Background and Purpose―We aimed to determine whether low-frequency electric field navigated repetitive transcranial\nmagnetic stimulation to noninjured motor cortex versus sham repetitive transcranial magnetic stimulation avoiding motor cortex could improve arm motor function in hemiplegic stroke patients when combined with motor training.\nMethods―Twelve outpatient US rehabilitation centers enrolled participants between May 2014 and December 2015. We delivered 1 Hz active or sham repetitive transcranial magnetic stimulation to noninjured motor cortex before each of eighteen 60-minute therapy sessions over a 6-week period, with outcomes measured at 1 week and 1, 3, and 6 months after end of treatment. The primary end point was the percentage of participants improving ≥5 points on upper extremity Fugl-Meyer score 6 months after end of treatment. Secondary analyses assessed changes on the upper extremity Fugl-Meyer and Action Research Arm Test and Wolf Motor Function Test and safety.\nResults―Of 199 participants, 167 completed treatment and follow-up because of early discontinuation of data collection. Upper extremity Fugl-Meyer gains were significant for experimental (P\u003c0.001) and sham groups (P\u003c0.001). Sixty-seven percent of the experimental group (95% CI, 58%–75%) and 65% of sham group (95% CI, 52%–76%) improved ≥5 points on 6-month upper extremity Fugl-Meyer (P=0.76). There was also no difference between experimental and sham groups in the Action Research Arm Test (P=0.80) or the Wolf Motor Function Test (P=0.55). A total of 26 serious adverse events occurred in 18 participants, with none related to the study or device, and with no difference between groups.\nConclusions―Among patients 3 to 12 months poststroke, goal-oriented motor rehabilitation improved motor function 6\nmonths after end of treatment. There was no difference between the active and sham repetitive transcranial magnetic stimulation trial arms.\nClinical Trial Registration―URL: https://www.clinicaltrials.gov. 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This article reviews the radiobiological, physical, technical and clinical aspects of IMRT for gastric, pancreatic, rectal and anal cancer, and summarizes the dosimetric and outcome studies to date.","grobid_abstract_attachment_id":53275939},"translated_abstract":null,"internal_url":"https://www.academia.edu/33194675/Role_of_intensity_modulated_radiation_therapy_in_gastrointestinal_cancer","translated_internal_url":"","created_at":"2017-05-25T08:39:58.802-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":53275939,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53275939/thumbnails/1.jpg","file_name":"bockbraderKim_2009_IMRTinGICancer.pdf","download_url":"https://www.academia.edu/attachments/53275939/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Role_of_intensity_modulated_radiation_th.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53275939/bockbraderKim_2009_IMRTinGICancer-libre.pdf?1495726855=\u0026response-content-disposition=attachment%3B+filename%3DRole_of_intensity_modulated_radiation_th.pdf\u0026Expires=1732437020\u0026Signature=bpWKm6mGiKA-52FpVnIU5Bgg8P9u9fUjprQpeN3k8FJJaIW6VcLlS0V18BF02v~zJOG-FaGIAXvkz4vdiys1hD3zFUpnBnAWJ6LIDmdlZqOKGhOYuWAU4aMRSuDFmlVINchUKVj03AcBfzrWNQ7aiA3yBmsNdvL7k3aoKy61ZyTgLmEl2gSymfyo-dLKLvcb5zQsgGs3BSUPNBg6iBMctQcyEo~tZhvwpAlBiF0q7xnb5gQkpXKqksU6JjXvzyOKsv2DVhYEK~NVKMxMp0hLkSoY6ZlBiui7lQNqZGcGS3E24EHpspAROb0DdfqNpZaywtowW2Lnr4YrTby6XZjmaA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Role_of_intensity_modulated_radiation_therapy_in_gastrointestinal_cancer","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":53275939,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53275939/thumbnails/1.jpg","file_name":"bockbraderKim_2009_IMRTinGICancer.pdf","download_url":"https://www.academia.edu/attachments/53275939/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Role_of_intensity_modulated_radiation_th.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53275939/bockbraderKim_2009_IMRTinGICancer-libre.pdf?1495726855=\u0026response-content-disposition=attachment%3B+filename%3DRole_of_intensity_modulated_radiation_th.pdf\u0026Expires=1732437020\u0026Signature=bpWKm6mGiKA-52FpVnIU5Bgg8P9u9fUjprQpeN3k8FJJaIW6VcLlS0V18BF02v~zJOG-FaGIAXvkz4vdiys1hD3zFUpnBnAWJ6LIDmdlZqOKGhOYuWAU4aMRSuDFmlVINchUKVj03AcBfzrWNQ7aiA3yBmsNdvL7k3aoKy61ZyTgLmEl2gSymfyo-dLKLvcb5zQsgGs3BSUPNBg6iBMctQcyEo~tZhvwpAlBiF0q7xnb5gQkpXKqksU6JjXvzyOKsv2DVhYEK~NVKMxMp0hLkSoY6ZlBiui7lQNqZGcGS3E24EHpspAROb0DdfqNpZaywtowW2Lnr4YrTby6XZjmaA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1454775,"name":"Rehabilitation of Cancer Patients","url":"https://www.academia.edu/Documents/in/Rehabilitation_of_Cancer_Patients"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="33194516"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/33194516/Big_data_challenges_in_decoding_cortical_activity_in_a_human_with_quadriplegia_to_inform_a_brain_computer_interface"><img alt="Research paper thumbnail of Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface" class="work-thumbnail" src="https://attachments.academia-assets.com/53275768/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/33194516/Big_data_challenges_in_decoding_cortical_activity_in_a_human_with_quadriplegia_to_inform_a_brain_computer_interface">Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">— Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed pa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">— Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="db9043f6a7c30d78ab0378cdb286df3b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53275768,"asset_id":33194516,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53275768/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="33194516"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="33194516"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33194516; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33194516]").text(description); $(".js-view-count[data-work-id=33194516]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33194516; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='33194516']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 33194516, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "db9043f6a7c30d78ab0378cdb286df3b" } } $('.js-work-strip[data-work-id=33194516]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":33194516,"title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface","translated_title":"","metadata":{"abstract":"— Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI."},"translated_abstract":"— Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. 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Mysiw","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"},{"id":29098185,"work_id":33194516,"tagging_user_id":7626683,"tagged_user_id":29925460,"co_author_invite_id":null,"email":"f***2@aol.com","affiliation":"Yeshiva University","display_order":5,"name":"David Friedenberg","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"},{"id":29098186,"work_id":33194516,"tagging_user_id":7626683,"tagged_user_id":47945078,"co_author_invite_id":null,"email":"m***2@gmail.com","display_order":6,"name":"mingming zhang","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"},{"id":29098187,"work_id":33194516,"tagging_user_id":7626683,"tagged_user_id":32735859,"co_author_invite_id":null,"email":"m***r@gmail.com","affiliation":"Ohio State University","display_order":7,"name":"Michael Schwemmer","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"},{"id":29098188,"work_id":33194516,"tagging_user_id":7626683,"tagged_user_id":47841492,"co_author_invite_id":null,"email":"a***n@battelle.org","display_order":8,"name":"Nicholas Annetta","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"},{"id":29098189,"work_id":33194516,"tagging_user_id":7626683,"tagged_user_id":3973077,"co_author_invite_id":null,"email":"f***5@yahoo.com","display_order":9,"name":"Ali Rezai","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"}],"downloadable_attachments":[{"id":53275768,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53275768/thumbnails/1.jpg","file_name":"Friedenberg_etal2016_Big_Data_Challenges_in_Decoding_Human_Brain_Data_to_Inform_a_Brain_Compu....pdf","download_url":"https://www.academia.edu/attachments/53275768/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Big_data_challenges_in_decoding_cortical.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53275768/Friedenberg_etal2016_Big_Data_Challenges_in_Decoding_Human_Brain_Data_to_Inform_a_Brain_Compu...-libre.pdf?1495726952=\u0026response-content-disposition=attachment%3B+filename%3DBig_data_challenges_in_decoding_cortical.pdf\u0026Expires=1732437020\u0026Signature=QjOiJcn1mRXOlOCb-FjJ5Q-KDVot2hPkqJAMUpzV~jZJ-VpZunVmczEGEcZXqkbc0OQ0BFXGbvhPcPwEO-z9Z1biMdlraBFOqwotMiMgnbZeaOmgFsvxxgE638MYpFgUimTqb4rux3uklksPwIeO3nG1ldruttk6UfA0ZBu55MGsAfz1VMQy25exz7M3ggJKdHaQUOZQqzuEHTvVAk2hX0A577iSSjYWwptlyDiderxP7M7sNfWKf2i2lUTypT3Z~mKS5xaiI-QYN6ftm-lB7FJKyetLcyRa5d77Wxh46tHcHrtbl91R9Z7hHuhbes9M0NM4~nSkUHzAD1CHnWFnRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Big_data_challenges_in_decoding_cortical_activity_in_a_human_with_quadriplegia_to_inform_a_brain_computer_interface","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":53275768,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53275768/thumbnails/1.jpg","file_name":"Friedenberg_etal2016_Big_Data_Challenges_in_Decoding_Human_Brain_Data_to_Inform_a_Brain_Compu....pdf","download_url":"https://www.academia.edu/attachments/53275768/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Big_data_challenges_in_decoding_cortical.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53275768/Friedenberg_etal2016_Big_Data_Challenges_in_Decoding_Human_Brain_Data_to_Inform_a_Brain_Compu...-libre.pdf?1495726952=\u0026response-content-disposition=attachment%3B+filename%3DBig_data_challenges_in_decoding_cortical.pdf\u0026Expires=1732437020\u0026Signature=QjOiJcn1mRXOlOCb-FjJ5Q-KDVot2hPkqJAMUpzV~jZJ-VpZunVmczEGEcZXqkbc0OQ0BFXGbvhPcPwEO-z9Z1biMdlraBFOqwotMiMgnbZeaOmgFsvxxgE638MYpFgUimTqb4rux3uklksPwIeO3nG1ldruttk6UfA0ZBu55MGsAfz1VMQy25exz7M3ggJKdHaQUOZQqzuEHTvVAk2hX0A577iSSjYWwptlyDiderxP7M7sNfWKf2i2lUTypT3Z~mKS5xaiI-QYN6ftm-lB7FJKyetLcyRa5d77Wxh46tHcHrtbl91R9Z7hHuhbes9M0NM4~nSkUHzAD1CHnWFnRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":47558,"name":"BCI","url":"https://www.academia.edu/Documents/in/BCI"},{"id":786962,"name":"BCI rehabilitation","url":"https://www.academia.edu/Documents/in/BCI_rehabilitation"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="33194515"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/33194515/Steady_state_visual_evoked_potential_abnormalities_in_schizophrenia"><img alt="Research paper thumbnail of Steady state visual evoked potential abnormalities in schizophrenia" class="work-thumbnail" src="https://attachments.academia-assets.com/53275796/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/33194515/Steady_state_visual_evoked_potential_abnormalities_in_schizophrenia">Steady state visual evoked potential abnormalities in schizophrenia</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objective: The steady state visual evoked potential (SSVEP) can be used to test the frequency res...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objective: The steady state visual evoked potential (SSVEP) can be used to test the frequency response function of neural circuits. Previous studies have shown reduced SSVEPs to alpha and lower frequencies of stimulation in schizophrenia. We investigated SSVEPs in schizophrenia at frequencies spanning the theta (4 Hz) to gamma (40 Hz) range. Methods: The SSVEPs to seven different frequencies of stimulation (4, 8, 17, 20, 23, 30 and 40 Hz) were obtained from 18 schizophrenia subjects and 33 healthy control subjects. Power at stimulating frequency (signal power) and power at frequencies above and below the stimulating frequency (noise power) were used to quantify the SSVEP responses. Results: Both groups showed an inverse relationship between power and frequency of stimulation. Schizophrenia subjects showed reduced signal power compared to healthy control subjects at higher frequencies (above 17 Hz), but not at 4 and 8 Hz at occipital region. Noise power was higher in schizophrenia subjects at frequencies between 4 and 20 Hz over occipital region and at 4, 17 and 20 Hz over frontal region. Conclusions: SSVEP signal power at beta and gamma frequencies of stimulation were reduced in schizophrenia. Schizophrenia subjects showed higher levels of EEG noise during photic stimulation at beta and lower frequencies. Significance: Inability to generate or maintain oscillations in neural networks may contribute to deficits in visual processing in schizophrenia.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="19feb859e9828bce98d028205bb17092" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53275796,"asset_id":33194515,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53275796/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="33194515"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="33194515"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33194515; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33194515]").text(description); $(".js-view-count[data-work-id=33194515]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33194515; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='33194515']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 33194515, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "19feb859e9828bce98d028205bb17092" } } $('.js-work-strip[data-work-id=33194515]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":33194515,"title":"Steady state visual evoked potential abnormalities in schizophrenia","translated_title":"","metadata":{"abstract":"Objective: The steady state visual evoked potential (SSVEP) can be used to test the frequency response function of neural circuits. Previous studies have shown reduced SSVEPs to alpha and lower frequencies of stimulation in schizophrenia. We investigated SSVEPs in schizophrenia at frequencies spanning the theta (4 Hz) to gamma (40 Hz) range. Methods: The SSVEPs to seven different frequencies of stimulation (4, 8, 17, 20, 23, 30 and 40 Hz) were obtained from 18 schizophrenia subjects and 33 healthy control subjects. Power at stimulating frequency (signal power) and power at frequencies above and below the stimulating frequency (noise power) were used to quantify the SSVEP responses. Results: Both groups showed an inverse relationship between power and frequency of stimulation. Schizophrenia subjects showed reduced signal power compared to healthy control subjects at higher frequencies (above 17 Hz), but not at 4 and 8 Hz at occipital region. Noise power was higher in schizophrenia subjects at frequencies between 4 and 20 Hz over occipital region and at 4, 17 and 20 Hz over frontal region. Conclusions: SSVEP signal power at beta and gamma frequencies of stimulation were reduced in schizophrenia. Schizophrenia subjects showed higher levels of EEG noise during photic stimulation at beta and lower frequencies. Significance: Inability to generate or maintain oscillations in neural networks may contribute to deficits in visual processing in schizophrenia."},"translated_abstract":"Objective: The steady state visual evoked potential (SSVEP) can be used to test the frequency response function of neural circuits. Previous studies have shown reduced SSVEPs to alpha and lower frequencies of stimulation in schizophrenia. We investigated SSVEPs in schizophrenia at frequencies spanning the theta (4 Hz) to gamma (40 Hz) range. Methods: The SSVEPs to seven different frequencies of stimulation (4, 8, 17, 20, 23, 30 and 40 Hz) were obtained from 18 schizophrenia subjects and 33 healthy control subjects. Power at stimulating frequency (signal power) and power at frequencies above and below the stimulating frequency (noise power) were used to quantify the SSVEP responses. Results: Both groups showed an inverse relationship between power and frequency of stimulation. Schizophrenia subjects showed reduced signal power compared to healthy control subjects at higher frequencies (above 17 Hz), but not at 4 and 8 Hz at occipital region. Noise power was higher in schizophrenia subjects at frequencies between 4 and 20 Hz over occipital region and at 4, 17 and 20 Hz over frontal region. Conclusions: SSVEP signal power at beta and gamma frequencies of stimulation were reduced in schizophrenia. Schizophrenia subjects showed higher levels of EEG noise during photic stimulation at beta and lower frequencies. Significance: Inability to generate or maintain oscillations in neural networks may contribute to deficits in visual processing in schizophrenia.","internal_url":"https://www.academia.edu/33194515/Steady_state_visual_evoked_potential_abnormalities_in_schizophrenia","translated_internal_url":"","created_at":"2017-05-25T08:26:44.649-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":29098236,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":43010706,"co_author_invite_id":null,"email":"a***r@iupui.edu","display_order":1,"name":"Anantha Shekhar","title":"Steady state visual evoked potential abnormalities in schizophrenia"},{"id":29098237,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":2,"name":"Marcia Bockbrader","title":"Steady state visual evoked potential abnormalities in schizophrenia"},{"id":29098238,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":28831504,"co_author_invite_id":null,"email":"w***k@indiana.edu","display_order":3,"name":"William Hetrick","title":"Steady state visual evoked potential abnormalities in schizophrenia"},{"id":29098239,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":37636881,"co_author_invite_id":null,"email":"j***s@iupui.edu","affiliation":"Indiana University","display_order":4,"name":"Jenifer Vohs","title":"Steady state visual evoked potential abnormalities in schizophrenia"},{"id":29098240,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":1959492,"email":"g***n@indiana.edu","display_order":5,"name":"Giri Krishnan","title":"Steady state visual evoked potential abnormalities in schizophrenia"},{"id":29098241,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":32793861,"co_author_invite_id":null,"email":"b***l@gmail.com","display_order":6,"name":"B. 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href="https://www.academia.edu/40495385/Upper_limb_sensorimotor_restoration_through_brain_computer_interface_technology_in_tetraparesis"><img alt="Research paper thumbnail of Upper limb sensorimotor restoration through brain-computer interface technology in tetraparesis" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40495385/Upper_limb_sensorimotor_restoration_through_brain_computer_interface_technology_in_tetraparesis">Upper limb sensorimotor restoration through brain-computer interface technology in tetraparesis</a></div><div class="wp-workCard_item"><span>Current Opinion in Biomedical Engineering</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">For individuals with spinal cord injury (SCI), brain-computer interface (BCI) technology offers a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">For individuals with spinal cord injury (SCI), brain-computer interface (BCI) technology offers a means to restore lost sensorimotor function by bridging disrupted neural pathways to reanimate paralyzed limbs. Restoring hand function is a high priority of those with tetraparesis due to the impact that manual dexterity has on independence and quality of life. However, to be useful in daily life, BCI systems need to deliver naturalistic and functional grasp speed, force, and dexterity. In clinical trials, individuals with paralysis have achieved the most dexterous control of grasp using either robotic neuroprosthetics or neuromuscular stimulation orthotics controlled by intracortical BCI systems. Next steps are in progress, with the development of portable components and decoding algorithm optimization to simplify setup and calibration.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40495385"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40495385"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40495385; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40495385]").text(description); $(".js-view-count[data-work-id=40495385]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40495385; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40495385']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40495385, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=40495385]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40495385,"title":"Upper limb sensorimotor restoration through brain-computer interface technology in tetraparesis","translated_title":"","metadata":{"doi":"10.1016/j.cobme.2019.09.002","abstract":"For individuals with spinal cord injury (SCI), brain-computer interface (BCI) technology offers a means to restore lost sensorimotor function by bridging disrupted neural pathways to reanimate paralyzed limbs. Restoring hand function is a high priority of those with tetraparesis due to the impact that manual dexterity has on independence and quality of life. However, to be useful in daily life, BCI systems need to deliver naturalistic and functional grasp speed, force, and dexterity. In clinical trials, individuals with paralysis have achieved the most dexterous control of grasp using either robotic neuroprosthetics or neuromuscular stimulation orthotics controlled by intracortical BCI systems. Next steps are in progress, with the development of portable components and decoding algorithm optimization to simplify setup and calibration.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Current Opinion in Biomedical Engineering"},"translated_abstract":"For individuals with spinal cord injury (SCI), brain-computer interface (BCI) technology offers a means to restore lost sensorimotor function by bridging disrupted neural pathways to reanimate paralyzed limbs. Restoring hand function is a high priority of those with tetraparesis due to the impact that manual dexterity has on independence and quality of life. However, to be useful in daily life, BCI systems need to deliver naturalistic and functional grasp speed, force, and dexterity. In clinical trials, individuals with paralysis have achieved the most dexterous control of grasp using either robotic neuroprosthetics or neuromuscular stimulation orthotics controlled by intracortical BCI systems. Next steps are in progress, with the development of portable components and decoding algorithm optimization to simplify setup and calibration.","internal_url":"https://www.academia.edu/40495385/Upper_limb_sensorimotor_restoration_through_brain_computer_interface_technology_in_tetraparesis","translated_internal_url":"","created_at":"2019-10-01T18:49:27.857-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Upper_limb_sensorimotor_restoration_through_brain_computer_interface_technology_in_tetraparesis","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[],"research_interests":[{"id":22824,"name":"Spinal Cord Injury","url":"https://www.academia.edu/Documents/in/Spinal_Cord_Injury"},{"id":46043,"name":"Functional Electrical Stimulation","url":"https://www.academia.edu/Documents/in/Functional_Electrical_Stimulation"},{"id":47558,"name":"BCI","url":"https://www.academia.edu/Documents/in/BCI"},{"id":84493,"name":"Neuroprosthetics","url":"https://www.academia.edu/Documents/in/Neuroprosthetics"}],"urls":[{"id":8862047,"url":"https://doi.org/10.1016/j.cobme.2019.09.002"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049465"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049465/Neural_Decoding_Algorithm_Requirements_for_a_Take_home_Brain_Computer_Interface"><img alt="Research paper thumbnail of Neural Decoding Algorithm Requirements for a Take-home Brain Computer Interface" class="work-thumbnail" src="https://attachments.academia-assets.com/60248868/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049465/Neural_Decoding_Algorithm_Requirements_for_a_Take_home_Brain_Computer_Interface">Neural Decoding Algorithm Requirements for a Take-home Brain Computer Interface</a></div><div class="wp-workCard_item"><span>Conf Proc IEEE Eng Med Biol Soc</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Brain computer interfaces (BCIs) have had several successful laboratory demonstrations, raising h...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Brain computer interfaces (BCIs) have had several successful laboratory demonstrations, raising hopes that a take-home system could improve the lives of patients in the future. However, challenges remain in translating BCI control of an assistive device in the lab into a robust take-home system. One challenge is designing neural decoders, algorithms that translate neural activity into control commands for a device, that meet BCI systems and extract requirements for neural decoding. Translating laboratory demonstrations of BCI systems to home-use requires careful consideration of patient priorities and signficant technical challenges. To address the former, potential users were surveyed on several BCI characteristics. We examine two such surveys and extract requirements for the technical challenge of BCI decoding, the algorithms that translate brain activity into actions. In one survey, potential users ranked non-invasiveness, daily setup time, independent operation, cost, number of functions provided, and response time as [1]. In another, the number of functions, simplicity of setup, accuracy, electrode type, setup time, and speed were all ranked with a median importance of least 9 out of 10 [2]. The characteristics directly impacted by the decoding algorithm fall into four main categories: setup time related to decoder training, number of functions provided, response time, and accuracy. The decoder-related setup time is primarily the time spent by the user calibrating the decoding algorithm to account for any day-today variability in the neural signals. It is typically performed on a pre-defined task where data is collected to train or update the decoding algorithms as the user performs the guided task. In [1] users expressed willingness to spend more time during initial training but want to minimize or even eliminate daily decoder setup time. Overall, a total setup time of 10-20 minutes would satisfy 65% of potential users, however that includes setup time not related to decoding [2].</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9f6f574cc6d2b2fd6258372315d4be65" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248868,"asset_id":40049465,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248868/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049465"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049465"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049465; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049465]").text(description); $(".js-view-count[data-work-id=40049465]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049465; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049465']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049465, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9f6f574cc6d2b2fd6258372315d4be65" } } $('.js-work-strip[data-work-id=40049465]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049465,"title":"Neural Decoding Algorithm Requirements for a Take-home Brain Computer Interface","translated_title":"","metadata":{"abstract":"Brain computer interfaces (BCIs) have had several successful laboratory demonstrations, raising hopes that a take-home system could improve the lives of patients in the future. However, challenges remain in translating BCI control of an assistive device in the lab into a robust take-home system. One challenge is designing neural decoders, algorithms that translate neural activity into control commands for a device, that meet BCI systems and extract requirements for neural decoding. Translating laboratory demonstrations of BCI systems to home-use requires careful consideration of patient priorities and signficant technical challenges. To address the former, potential users were surveyed on several BCI characteristics. We examine two such surveys and extract requirements for the technical challenge of BCI decoding, the algorithms that translate brain activity into actions. In one survey, potential users ranked non-invasiveness, daily setup time, independent operation, cost, number of functions provided, and response time as [1]. In another, the number of functions, simplicity of setup, accuracy, electrode type, setup time, and speed were all ranked with a median importance of least 9 out of 10 [2]. The characteristics directly impacted by the decoding algorithm fall into four main categories: setup time related to decoder training, number of functions provided, response time, and accuracy. The decoder-related setup time is primarily the time spent by the user calibrating the decoding algorithm to account for any day-today variability in the neural signals. It is typically performed on a pre-defined task where data is collected to train or update the decoding algorithms as the user performs the guided task. In [1] users expressed willingness to spend more time during initial training but want to minimize or even eliminate daily decoder setup time. Overall, a total setup time of 10-20 minutes would satisfy 65% of potential users, however that includes setup time not related to decoding [2].","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Conf Proc IEEE Eng Med Biol Soc"},"translated_abstract":"Brain computer interfaces (BCIs) have had several successful laboratory demonstrations, raising hopes that a take-home system could improve the lives of patients in the future. However, challenges remain in translating BCI control of an assistive device in the lab into a robust take-home system. One challenge is designing neural decoders, algorithms that translate neural activity into control commands for a device, that meet BCI systems and extract requirements for neural decoding. Translating laboratory demonstrations of BCI systems to home-use requires careful consideration of patient priorities and signficant technical challenges. To address the former, potential users were surveyed on several BCI characteristics. We examine two such surveys and extract requirements for the technical challenge of BCI decoding, the algorithms that translate brain activity into actions. In one survey, potential users ranked non-invasiveness, daily setup time, independent operation, cost, number of functions provided, and response time as [1]. In another, the number of functions, simplicity of setup, accuracy, electrode type, setup time, and speed were all ranked with a median importance of least 9 out of 10 [2]. The characteristics directly impacted by the decoding algorithm fall into four main categories: setup time related to decoder training, number of functions provided, response time, and accuracy. The decoder-related setup time is primarily the time spent by the user calibrating the decoding algorithm to account for any day-today variability in the neural signals. It is typically performed on a pre-defined task where data is collected to train or update the decoding algorithms as the user performs the guided task. In [1] users expressed willingness to spend more time during initial training but want to minimize or even eliminate daily decoder setup time. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049456"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049456/Clinical_performance_evaluation_for_a_take_home_brain_computer_interface_for_grasp"><img alt="Research paper thumbnail of Clinical performance evaluation for a take-home brain computer interface for grasp" class="work-thumbnail" src="https://attachments.academia-assets.com/60248830/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049456/Clinical_performance_evaluation_for_a_take_home_brain_computer_interface_for_grasp">Clinical performance evaluation for a take-home brain computer interface for grasp</a></div><div class="wp-workCard_item"><span>Conf Proc IEEE Eng Med Biol Soc</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Brain computer interfaces (BCIs) have successfully been used in laboratory settings to restore up...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Brain computer interfaces (BCIs) have successfully been used in laboratory settings to restore upper limb motor function to individuals paralyzed from spinal cord injury. However, translation into neuroprosthetics for home use requires optimization informed by patient-centered design. Here, we review patient priorities from the literature and describe GAIN, a patient-centric framework for evaluating clinical performance of BCI-grasp neuroprosthetics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="781cefb630ac8d06425a3f2210d457c0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248830,"asset_id":40049456,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248830/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049456"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049456"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049456; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049456]").text(description); $(".js-view-count[data-work-id=40049456]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049456; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049456']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049456, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "781cefb630ac8d06425a3f2210d457c0" } } $('.js-work-strip[data-work-id=40049456]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049456,"title":"Clinical performance evaluation for a take-home brain computer interface for grasp","translated_title":"","metadata":{"abstract":"Brain computer interfaces (BCIs) have successfully been used in laboratory settings to restore upper limb motor function to individuals paralyzed from spinal cord injury. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049317"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049317/Brain_Computer_Interfaces_in_Rehabilitation_Medicine"><img alt="Research paper thumbnail of Brain Computer Interfaces in Rehabilitation Medicine" class="work-thumbnail" src="https://attachments.academia-assets.com/60248794/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049317/Brain_Computer_Interfaces_in_Rehabilitation_Medicine">Brain Computer Interfaces in Rehabilitation Medicine</a></div><div class="wp-workCard_item"><span>PM&R</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One innovation currently influencing physical medicine and rehabilitation is brainecomputer inter...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One innovation currently influencing physical medicine and rehabilitation is brainecomputer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a6b8307d093e3543b03febdae816f666" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248794,"asset_id":40049317,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248794/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049317"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049317"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049317; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049317]").text(description); $(".js-view-count[data-work-id=40049317]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049317; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049317']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049317, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a6b8307d093e3543b03febdae816f666" } } $('.js-work-strip[data-work-id=40049317]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049317,"title":"Brain Computer Interfaces in Rehabilitation Medicine","translated_title":"","metadata":{"doi":"10.1016/j.pmrj.2018.05.028","abstract":"One innovation currently influencing physical medicine and rehabilitation is brainecomputer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. 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An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.","internal_url":"https://www.academia.edu/40049317/Brain_Computer_Interfaces_in_Rehabilitation_Medicine","translated_internal_url":"","created_at":"2019-08-09T15:11:48.234-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892999,"work_id":40049317,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":1,"name":"Marcia Bockbrader","title":"Brain Computer Interfaces in Rehabilitation Medicine"}],"downloadable_attachments":[{"id":60248794,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248794/thumbnails/1.jpg","file_name":"Bockbrader_etal2018_BCIinRehab20190809-29399-q687av.pdf","download_url":"https://www.academia.edu/attachments/60248794/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Brain_Computer_Interfaces_in_Rehabilitat.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248794/Bockbrader_etal2018_BCIinRehab20190809-29399-q687av-libre.pdf?1565390551=\u0026response-content-disposition=attachment%3B+filename%3DBrain_Computer_Interfaces_in_Rehabilitat.pdf\u0026Expires=1732437019\u0026Signature=aaWj2usKUxQBpio1fj446e6Ugfk6riLdkbXnR-w4JNJB~1D78JCCds0-hhuwrjxbX7SbpNZA7ltkzIDUYXh-jbqRo0mh9cM3DUzPJa6HIC3xgray0qkB-oRQkRGcJGIJiAPzFAzTf8l0O7kxPXXqIr4KObfCbfaQZPbpxKCjefsdhgxKWQuT3NI2~qrw1ReiZe7TpOTPof3rcMxgilqJfVCAYJ9Sg4abO7V1EgxRmgG2AHo4vF1GXp4aVGJu9wqgCCJ4tvylbg0sz78XNYgQU2DkK6pwAXYYaWcWiKJRm-bpZkUiCKpI58lyNJU84X9zYR~Qef00pDUMBRtQN-NqFw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Brain_Computer_Interfaces_in_Rehabilitation_Medicine","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248794,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248794/thumbnails/1.jpg","file_name":"Bockbrader_etal2018_BCIinRehab20190809-29399-q687av.pdf","download_url":"https://www.academia.edu/attachments/60248794/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Brain_Computer_Interfaces_in_Rehabilitat.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248794/Bockbrader_etal2018_BCIinRehab20190809-29399-q687av-libre.pdf?1565390551=\u0026response-content-disposition=attachment%3B+filename%3DBrain_Computer_Interfaces_in_Rehabilitat.pdf\u0026Expires=1732437019\u0026Signature=aaWj2usKUxQBpio1fj446e6Ugfk6riLdkbXnR-w4JNJB~1D78JCCds0-hhuwrjxbX7SbpNZA7ltkzIDUYXh-jbqRo0mh9cM3DUzPJa6HIC3xgray0qkB-oRQkRGcJGIJiAPzFAzTf8l0O7kxPXXqIr4KObfCbfaQZPbpxKCjefsdhgxKWQuT3NI2~qrw1ReiZe7TpOTPof3rcMxgilqJfVCAYJ9Sg4abO7V1EgxRmgG2AHo4vF1GXp4aVGJu9wqgCCJ4tvylbg0sz78XNYgQU2DkK6pwAXYYaWcWiKJRm-bpZkUiCKpI58lyNJU84X9zYR~Qef00pDUMBRtQN-NqFw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":2256,"name":"Rehabilitation","url":"https://www.academia.edu/Documents/in/Rehabilitation"},{"id":3946,"name":"Neurorehabilitation","url":"https://www.academia.edu/Documents/in/Neurorehabilitation"},{"id":32001,"name":"Physical Medicine and Rehabilitation","url":"https://www.academia.edu/Documents/in/Physical_Medicine_and_Rehabilitation"},{"id":256326,"name":"Neurotechnology","url":"https://www.academia.edu/Documents/in/Neurotechnology"},{"id":644153,"name":"Brain Computer Interface BCI","url":"https://www.academia.edu/Documents/in/Brain_Computer_Interface_BCI"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049306"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049306/Does_Ultrasound_Enhanced_Instruction_of_Musculoskeletal_Anatomy_Improve_Physical_Examination_Skills_of_First_Year_Medical_Students"><img alt="Research paper thumbnail of Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical Students" class="work-thumbnail" src="https://attachments.academia-assets.com/60248785/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049306/Does_Ultrasound_Enhanced_Instruction_of_Musculoskeletal_Anatomy_Improve_Physical_Examination_Skills_of_First_Year_Medical_Students">Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical Students</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://osu.academia.edu/MarcieBockbrader">Marcie Bockbrader</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://osu1.academia.edu/DavidWay">David Way</a></span></div><div class="wp-workCard_item"><span>Journal of Ultrasound in Medicine</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objectives-Ultrasound imaging is commonly used to teach basic anatomy to medical students. The pu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objectives-Ultrasound imaging is commonly used to teach basic anatomy to medical students. The purpose of this study was to determine whether learning musculo-skeletal anatomy with ultrasound improved performance on medical students' musculoskeletal physical examination skills. Methods-Twenty-seven first-year medical students were randomly assigned to 1 of 2 instructional groups: either shoulder or knee. Both groups received a lecture followed by hands-on ultrasound scanning on live human models of the assigned joint. After instruction, students were assessed on their ability to accurately palpate 4 ana-tomic landmarks: the acromioclavicular joint, the proximal long-head biceps tendon, and the medial and lateral joint lines of the knee. Performance scores were based on both accuracy and time. A total physical examination performance score was derived for each joint. Scores for instructional groups were compared by a 2-way analysis of variance with 1 repeated measure. Significant findings were further analyzed with post hoc tests. Results-All students performed significantly better on the knee examination, irrespective of instructional group (F 5 14.9; df 5 1.25; P 5 .001). Moreover, the shoulder instruction group performed significantly better than the knee group on the overall assessment (t 5-3.0; df 5 25; P < .01). Post hoc analyses revealed that differences in group performance were due to the shoulder group's higher scores on palpation of the biceps tendon (t 5-2.8; df 5 25; P 5 .01), a soft tissue landmark. Both groups performed similarly on palpation of all other anatomic structures. Conclusions-The use of ultrasound appears to provide an educational advantage when learning musculoskeletal physical examination of soft tissue landmarks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2b3ec1036c1ed9a61e70fb988facc055" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248785,"asset_id":40049306,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248785/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049306"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049306"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049306; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049306]").text(description); $(".js-view-count[data-work-id=40049306]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049306; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049306']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049306, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2b3ec1036c1ed9a61e70fb988facc055" } } $('.js-work-strip[data-work-id=40049306]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049306,"title":"Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical Students","translated_title":"","metadata":{"doi":"10.1002/jum.14322","abstract":"Objectives-Ultrasound imaging is commonly used to teach basic anatomy to medical students. The purpose of this study was to determine whether learning musculo-skeletal anatomy with ultrasound improved performance on medical students' musculoskeletal physical examination skills. Methods-Twenty-seven first-year medical students were randomly assigned to 1 of 2 instructional groups: either shoulder or knee. Both groups received a lecture followed by hands-on ultrasound scanning on live human models of the assigned joint. After instruction, students were assessed on their ability to accurately palpate 4 ana-tomic landmarks: the acromioclavicular joint, the proximal long-head biceps tendon, and the medial and lateral joint lines of the knee. Performance scores were based on both accuracy and time. A total physical examination performance score was derived for each joint. Scores for instructional groups were compared by a 2-way analysis of variance with 1 repeated measure. Significant findings were further analyzed with post hoc tests. Results-All students performed significantly better on the knee examination, irrespective of instructional group (F 5 14.9; df 5 1.25; P 5 .001). Moreover, the shoulder instruction group performed significantly better than the knee group on the overall assessment (t 5-3.0; df 5 25; P \u003c .01). Post hoc analyses revealed that differences in group performance were due to the shoulder group's higher scores on palpation of the biceps tendon (t 5-2.8; df 5 25; P 5 .01), a soft tissue landmark. Both groups performed similarly on palpation of all other anatomic structures. Conclusions-The use of ultrasound appears to provide an educational advantage when learning musculoskeletal physical examination of soft tissue landmarks.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Journal of Ultrasound in Medicine"},"translated_abstract":"Objectives-Ultrasound imaging is commonly used to teach basic anatomy to medical students. The purpose of this study was to determine whether learning musculo-skeletal anatomy with ultrasound improved performance on medical students' musculoskeletal physical examination skills. Methods-Twenty-seven first-year medical students were randomly assigned to 1 of 2 instructional groups: either shoulder or knee. Both groups received a lecture followed by hands-on ultrasound scanning on live human models of the assigned joint. After instruction, students were assessed on their ability to accurately palpate 4 ana-tomic landmarks: the acromioclavicular joint, the proximal long-head biceps tendon, and the medial and lateral joint lines of the knee. Performance scores were based on both accuracy and time. A total physical examination performance score was derived for each joint. Scores for instructional groups were compared by a 2-way analysis of variance with 1 repeated measure. Significant findings were further analyzed with post hoc tests. Results-All students performed significantly better on the knee examination, irrespective of instructional group (F 5 14.9; df 5 1.25; P 5 .001). Moreover, the shoulder instruction group performed significantly better than the knee group on the overall assessment (t 5-3.0; df 5 25; P \u003c .01). Post hoc analyses revealed that differences in group performance were due to the shoulder group's higher scores on palpation of the biceps tendon (t 5-2.8; df 5 25; P 5 .01), a soft tissue landmark. Both groups performed similarly on palpation of all other anatomic structures. Conclusions-The use of ultrasound appears to provide an educational advantage when learning musculoskeletal physical examination of soft tissue landmarks.","internal_url":"https://www.academia.edu/40049306/Does_Ultrasound_Enhanced_Instruction_of_Musculoskeletal_Anatomy_Improve_Physical_Examination_Skills_of_First_Year_Medical_Students","translated_internal_url":"","created_at":"2019-08-09T15:09:43.443-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892991,"work_id":40049306,"tagging_user_id":7626683,"tagged_user_id":122763331,"co_author_invite_id":6885052,"email":"a***1@alumni.nd.edu","display_order":1,"name":"Allison Schroeder","title":"Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical 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src="https://attachments.academia-assets.com/60248778/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049300/Dexterous_Control_of_Seven_Functional_Hand_Movements_Using_Cortically_Controlled_Transcutaneous_Muscle_Stimulation_in_a_Person_With_Tetraplegia">Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia</a></div><div class="wp-workCard_item"><span>Frontiers in Neuroscience </span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to r...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with >95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="11d4a683243a043e6b4581de6f4f930f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248778,"asset_id":40049300,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248778/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049300"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049300"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049300; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049300]").text(description); $(".js-view-count[data-work-id=40049300]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049300; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049300']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049300, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "11d4a683243a043e6b4581de6f4f930f" } } $('.js-work-strip[data-work-id=40049300]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049300,"title":"Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia","translated_title":"","metadata":{"doi":"10.3389/fnins.2018.00208","abstract":"Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with \u003e95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Frontiers in Neuroscience "},"translated_abstract":"Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with \u003e95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.","internal_url":"https://www.academia.edu/40049300/Dexterous_Control_of_Seven_Functional_Hand_Movements_Using_Cortically_Controlled_Transcutaneous_Muscle_Stimulation_in_a_Person_With_Tetraplegia","translated_internal_url":"","created_at":"2019-08-09T15:07:46.540-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892986,"work_id":40049300,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":6885050,"email":"s***g@battelle.org","display_order":1,"name":"Gaurav Sharma","title":"Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia"},{"id":32892987,"work_id":40049300,"tagging_user_id":7626683,"tagged_user_id":43069448,"co_author_invite_id":null,"email":"s***s@osumc.edu","display_order":2,"name":"Sam Colachis","title":"Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia"},{"id":32892988,"work_id":40049300,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":6327072,"email":"b***3@osu.edu","display_order":3,"name":"Marcie Bockbrader","title":"Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia"},{"id":32892989,"work_id":40049300,"tagging_user_id":7626683,"tagged_user_id":47636515,"co_author_invite_id":null,"email":"f***d@battelle.org","display_order":4,"name":"David Friedenberg","title":"Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia"},{"id":32892990,"work_id":40049300,"tagging_user_id":7626683,"tagged_user_id":38974648,"co_author_invite_id":null,"email":"w***w@osumc.edu","display_order":5,"name":"W. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049275"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049275/Extracting_wavelet_based_neural_features_from_human_intracortical_recordings_for_neuroprosthetics_applications"><img alt="Research paper thumbnail of Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications" class="work-thumbnail" src="https://attachments.academia-assets.com/60248750/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049275/Extracting_wavelet_based_neural_features_from_human_intracortical_recordings_for_neuroprosthetics_applications">Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications</a></div><div class="wp-workCard_item"><span>Bioelectronic Medicine</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background: Understanding the long-term behavior of intracortically-recorded signals is essential...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain.<br />Methods: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate<br />the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor<br />cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean<br />wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP<br />(lf-MWP, 0–234 Hz), mid-frequency MWP (mf-MWP, 234 Hz–3.75 kHz) and high-frequency MWP (hf-MWP, >3.75 kHz).<br />We analyzed these features using data collected from two experiments that were repeated over the course of about<br />3 years and compared their signal stability and decoding performance with the more standard threshold crossings,<br />local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings.<br />Results: All neural features could stably track neural information for over 3 years post-implantation and were less<br />prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector<br />machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively,<br />in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings.<br />Conclusions: Our results suggest that usingMWP features in the appropriate frequency bands can provide an effective<br />neural feature for brain computer interface intended for chronic applications.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c3ff2d6acad89b88c36cd0a563913def" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248750,"asset_id":40049275,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248750/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049275"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049275"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049275; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049275]").text(description); $(".js-view-count[data-work-id=40049275]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049275; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049275']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049275, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c3ff2d6acad89b88c36cd0a563913def" } } $('.js-work-strip[data-work-id=40049275]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049275,"title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications","translated_title":"","metadata":{"doi":"10.1186/s42234-018-0011-x","abstract":"Background: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain.\nMethods: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate\nthe utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor\ncortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean\nwavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP\n(lf-MWP, 0–234 Hz), mid-frequency MWP (mf-MWP, 234 Hz–3.75 kHz) and high-frequency MWP (hf-MWP, \u003e3.75 kHz).\nWe analyzed these features using data collected from two experiments that were repeated over the course of about\n3 years and compared their signal stability and decoding performance with the more standard threshold crossings,\nlocal field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings.\nResults: All neural features could stably track neural information for over 3 years post-implantation and were less\nprone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector\nmachine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively,\nin classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings.\nConclusions: Our results suggest that usingMWP features in the appropriate frequency bands can provide an effective\nneural feature for brain computer interface intended for chronic applications.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Bioelectronic Medicine"},"translated_abstract":"Background: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain.\nMethods: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate\nthe utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor\ncortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean\nwavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP\n(lf-MWP, 0–234 Hz), mid-frequency MWP (mf-MWP, 234 Hz–3.75 kHz) and high-frequency MWP (hf-MWP, \u003e3.75 kHz).\nWe analyzed these features using data collected from two experiments that were repeated over the course of about\n3 years and compared their signal stability and decoding performance with the more standard threshold crossings,\nlocal field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings.\nResults: All neural features could stably track neural information for over 3 years post-implantation and were less\nprone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector\nmachine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively,\nin classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings.\nConclusions: Our results suggest that usingMWP features in the appropriate frequency bands can provide an effective\nneural feature for brain computer interface intended for chronic applications.","internal_url":"https://www.academia.edu/40049275/Extracting_wavelet_based_neural_features_from_human_intracortical_recordings_for_neuroprosthetics_applications","translated_internal_url":"","created_at":"2019-08-09T15:02:33.916-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892980,"work_id":40049275,"tagging_user_id":7626683,"tagged_user_id":47636515,"co_author_invite_id":null,"email":"f***d@battelle.org","display_order":1,"name":"David Friedenberg","title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications"},{"id":32892981,"work_id":40049275,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":2,"name":"Marcia Bockbrader","title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications"},{"id":32892982,"work_id":40049275,"tagging_user_id":7626683,"tagged_user_id":38974648,"co_author_invite_id":null,"email":"w***w@osumc.edu","display_order":3,"name":"W. Mysiw","title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications"},{"id":32892983,"work_id":40049275,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":4404282,"email":"b***n@battelle.org","display_order":4,"name":"Chad Bouton","title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications"},{"id":32892984,"work_id":40049275,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":6885050,"email":"s***g@battelle.org","display_order":5,"name":"Gaurav Sharma","title":"Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications"}],"downloadable_attachments":[{"id":60248750,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248750/thumbnails/1.jpg","file_name":"Zhang_etal2018_chronic20190809-73635-a0kwzo.pdf","download_url":"https://www.academia.edu/attachments/60248750/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Extracting_wavelet_based_neural_features.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248750/Zhang_etal2018_chronic20190809-73635-a0kwzo-libre.pdf?1565388924=\u0026response-content-disposition=attachment%3B+filename%3DExtracting_wavelet_based_neural_features.pdf\u0026Expires=1732225872\u0026Signature=AuzBL3NAUdxPfTTgFwepDkJmVgeY58mCdufjkZbejBxhIwskmPss9wbncwUb~HXPFwAZqjRkHrNTRBidNwrzmAY4Cb49lgYXjTRfen24ft8TtUeAhSVAwqsjKaKSm0k7Zbx9wuYrNvcLbhTYgO3YHDlNobMNmPlNgOPoH98pIHQxkyUIrzvKgfiKxeUKF4Ai~xZsgIEmgmO1Vq5tTNLK5tn319WU8IUOnWUCrSeWTwMAkGamZaxNRJgvHoY73aMFG95gkXTNr8eT-rodP-w-Ju81A2mmFl-8u3~tVCabg1U2C5Xv9acdvtuNJ2o8mbrwaEWSLQ-h0cyXaeDFPFxhjQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Extracting_wavelet_based_neural_features_from_human_intracortical_recordings_for_neuroprosthetics_applications","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248750,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248750/thumbnails/1.jpg","file_name":"Zhang_etal2018_chronic20190809-73635-a0kwzo.pdf","download_url":"https://www.academia.edu/attachments/60248750/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Extracting_wavelet_based_neural_features.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248750/Zhang_etal2018_chronic20190809-73635-a0kwzo-libre.pdf?1565388924=\u0026response-content-disposition=attachment%3B+filename%3DExtracting_wavelet_based_neural_features.pdf\u0026Expires=1732225872\u0026Signature=AuzBL3NAUdxPfTTgFwepDkJmVgeY58mCdufjkZbejBxhIwskmPss9wbncwUb~HXPFwAZqjRkHrNTRBidNwrzmAY4Cb49lgYXjTRfen24ft8TtUeAhSVAwqsjKaKSm0k7Zbx9wuYrNvcLbhTYgO3YHDlNobMNmPlNgOPoH98pIHQxkyUIrzvKgfiKxeUKF4Ai~xZsgIEmgmO1Vq5tTNLK5tn319WU8IUOnWUCrSeWTwMAkGamZaxNRJgvHoY73aMFG95gkXTNr8eT-rodP-w-Ju81A2mmFl-8u3~tVCabg1U2C5Xv9acdvtuNJ2o8mbrwaEWSLQ-h0cyXaeDFPFxhjQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":15811,"name":"Biomedical signal and image processing","url":"https://www.academia.edu/Documents/in/Biomedical_signal_and_image_processing"},{"id":91365,"name":"Wavelet Transforms","url":"https://www.academia.edu/Documents/in/Wavelet_Transforms"},{"id":384468,"name":"Bio-implantable Circuits and Neural Signal Processing","url":"https://www.academia.edu/Documents/in/Bio-implantable_Circuits_and_Neural_Signal_Processing"},{"id":644153,"name":"Brain Computer Interface BCI","url":"https://www.academia.edu/Documents/in/Brain_Computer_Interface_BCI"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049262"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049262/Meeting_brain_computer_interface_user_performance_expectations_using_a_deep_neural_network_decoding_framework"><img alt="Research paper thumbnail of Meeting brain-computer interface user performance expectations using a deep neural network decoding framework" class="work-thumbnail" src="https://attachments.academia-assets.com/60248738/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049262/Meeting_brain_computer_interface_user_performance_expectations_using_a_deep_neural_network_decoding_framework">Meeting brain-computer interface user performance expectations using a deep neural network decoding framework</a></div><div class="wp-workCard_item"><span>Nature Medicine</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices 1-9. Surveys of potential end-users have identified key BCI system features 10-14 , including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm 1,15 , which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network 16 decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracorti-cal data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure 3,17-20 , responds faster than competing methods 8 , and can increase functionality with minimal retraining by using a technique known as transfer learning 21. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT) 22. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology. The study participant was a 27-year-old male with C5 AIS A tetraplegia due to spinal cord injury. He was implanted with a 96-channel microelectrode array in the hand and arm area of his left primary motor cortex 8,23,24. We trained and evaluated BCI decoders using 80 sessions of intracortical data collected from the participant over 865 d. During each session, the participant performed two 104-s blocks of the four-movement task (Methods), in which he was cued to imagine a series of four distinct hand movements (index extension, index flexion, wrist extension, wrist flexion) in a random order (Fig. 1a,b). We calibrated the initial neural network (NN) model using 40 sessions (80 blocks) from the training period (Fig. 1c). As the model was not updated at all over the subsequent test period, we call it the fixed NN (fNN). Two additional NN models were created from the fNN using updating procedures that used the first block of each of the 40 sessions in the testing period in different ways (Fig. 1c): supervised updating (sNN) or unsupervised updating (uNN) (Fig. 1d and Methods). In this context, supervised refers to the algorithm using explicit training labels (i.e., known timing and type of intended action) as opposed to unsupervised, in which the timing and type of intended action were unknown, as occurs with general BCI use. For comparison, the first block of each of the 40 sessions in the testing period was also used to calibrate benchmark BCI decoders that were retrained daily: a support vector machine (SVM) decoder (Fig. 1d) 8,23,25,26 , a linear discriminant analysis (LDA) decoder 17 , and a naive Bayes decoder 18. The SVM performed better than the LDA or naive Bayes decoder (Supplementary Fig. 1) and was thus used for further comparisons with NN performance. Neural features used by all models were the mean wavelet power (MWP) values calculated from raw voltage for each of the 96 channels over 100-ms bins 8,23,24 (Fig. 1e, Supplementary Fig. 2, and Methods). Performance for each of the NN and comparison models was initially evaluated using accuracy (percentage of correctly predicted time-bins) on the second block of data from each session during the testing period (Methods). Figure 1e shows data processing steps and NN model architecture. To quantify improved BCI accuracy with the NN, we compared the performance of the supervised, daily-updated sNN against a daily-retrained SVM. Figure 2a,b shows that the sNN was more accurate than the daily-retrained SVM for all sessions, with a mean difference of 6.35 ± 2.47% (mean ± s.d.; P = 3.69 × 20-8 , V = 820, n = 40 paired two-sided Wilcoxon signed rank test; n is the sample size and V is the test statistic for the paired Wilcoxon test). In addition, for 37 out of 40 sessions, the sNN accuracy was > 90%, indicating consistently high performance in accordance with user expectations 13. In contrast, the SVM accuracy was > 90% for only 12 sessions. To demonstrate that a BCI with a neural network decoder (NN-BCI) could sustain high accuracy for over a year without the need of supervised updating (thus reducing daily setup time), we evaluated performance of the fNN. Figure 2c shows that the fNN was more accurate than the daily-retrained SVM for 36 out of 40 test sessions, with a mean difference of 4.56. ± 3.06% (P = 1.90 × 10-7 , V = 798, n = 40; Fig. 2c, inset). In addition, the fNN accuracy was > 90% for 32 sessions. Not only was the fNN able to sustain high accuracy decoding performance for over a year (381 d) without being recalibrated, it significantly outperformed all fixed versions of the benchmark decoders we tested (Supplementary Fig. 1). However, the fNN accuracy was lower than that of the sNN (Fig. 2d), which received supervised updates throughout the testing period. In fact,</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9c08762c780753719b8305dcc353ff87" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248738,"asset_id":40049262,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248738/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049262"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049262"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049262; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049262]").text(description); $(".js-view-count[data-work-id=40049262]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049262; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049262']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049262, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9c08762c780753719b8305dcc353ff87" } } $('.js-work-strip[data-work-id=40049262]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049262,"title":"Meeting brain-computer interface user performance expectations using a deep neural network decoding framework","translated_title":"","metadata":{"doi":"10.1038/s41591-018-0171-y","abstract":"Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices 1-9. Surveys of potential end-users have identified key BCI system features 10-14 , including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm 1,15 , which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network 16 decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracorti-cal data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure 3,17-20 , responds faster than competing methods 8 , and can increase functionality with minimal retraining by using a technique known as transfer learning 21. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT) 22. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology. The study participant was a 27-year-old male with C5 AIS A tetraplegia due to spinal cord injury. He was implanted with a 96-channel microelectrode array in the hand and arm area of his left primary motor cortex 8,23,24. We trained and evaluated BCI decoders using 80 sessions of intracortical data collected from the participant over 865 d. During each session, the participant performed two 104-s blocks of the four-movement task (Methods), in which he was cued to imagine a series of four distinct hand movements (index extension, index flexion, wrist extension, wrist flexion) in a random order (Fig. 1a,b). We calibrated the initial neural network (NN) model using 40 sessions (80 blocks) from the training period (Fig. 1c). As the model was not updated at all over the subsequent test period, we call it the fixed NN (fNN). Two additional NN models were created from the fNN using updating procedures that used the first block of each of the 40 sessions in the testing period in different ways (Fig. 1c): supervised updating (sNN) or unsupervised updating (uNN) (Fig. 1d and Methods). In this context, supervised refers to the algorithm using explicit training labels (i.e., known timing and type of intended action) as opposed to unsupervised, in which the timing and type of intended action were unknown, as occurs with general BCI use. For comparison, the first block of each of the 40 sessions in the testing period was also used to calibrate benchmark BCI decoders that were retrained daily: a support vector machine (SVM) decoder (Fig. 1d) 8,23,25,26 , a linear discriminant analysis (LDA) decoder 17 , and a naive Bayes decoder 18. The SVM performed better than the LDA or naive Bayes decoder (Supplementary Fig. 1) and was thus used for further comparisons with NN performance. Neural features used by all models were the mean wavelet power (MWP) values calculated from raw voltage for each of the 96 channels over 100-ms bins 8,23,24 (Fig. 1e, Supplementary Fig. 2, and Methods). Performance for each of the NN and comparison models was initially evaluated using accuracy (percentage of correctly predicted time-bins) on the second block of data from each session during the testing period (Methods). Figure 1e shows data processing steps and NN model architecture. To quantify improved BCI accuracy with the NN, we compared the performance of the supervised, daily-updated sNN against a daily-retrained SVM. Figure 2a,b shows that the sNN was more accurate than the daily-retrained SVM for all sessions, with a mean difference of 6.35 ± 2.47% (mean ± s.d.; P = 3.69 × 20-8 , V = 820, n = 40 paired two-sided Wilcoxon signed rank test; n is the sample size and V is the test statistic for the paired Wilcoxon test). In addition, for 37 out of 40 sessions, the sNN accuracy was \u003e 90%, indicating consistently high performance in accordance with user expectations 13. In contrast, the SVM accuracy was \u003e 90% for only 12 sessions. To demonstrate that a BCI with a neural network decoder (NN-BCI) could sustain high accuracy for over a year without the need of supervised updating (thus reducing daily setup time), we evaluated performance of the fNN. Figure 2c shows that the fNN was more accurate than the daily-retrained SVM for 36 out of 40 test sessions, with a mean difference of 4.56. ± 3.06% (P = 1.90 × 10-7 , V = 798, n = 40; Fig. 2c, inset). In addition, the fNN accuracy was \u003e 90% for 32 sessions. Not only was the fNN able to sustain high accuracy decoding performance for over a year (381 d) without being recalibrated, it significantly outperformed all fixed versions of the benchmark decoders we tested (Supplementary Fig. 1). However, the fNN accuracy was lower than that of the sNN (Fig. 2d), which received supervised updates throughout the testing period. In fact,","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Nature Medicine"},"translated_abstract":"Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices 1-9. Surveys of potential end-users have identified key BCI system features 10-14 , including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm 1,15 , which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network 16 decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracorti-cal data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure 3,17-20 , responds faster than competing methods 8 , and can increase functionality with minimal retraining by using a technique known as transfer learning 21. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT) 22. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology. The study participant was a 27-year-old male with C5 AIS A tetraplegia due to spinal cord injury. He was implanted with a 96-channel microelectrode array in the hand and arm area of his left primary motor cortex 8,23,24. We trained and evaluated BCI decoders using 80 sessions of intracortical data collected from the participant over 865 d. During each session, the participant performed two 104-s blocks of the four-movement task (Methods), in which he was cued to imagine a series of four distinct hand movements (index extension, index flexion, wrist extension, wrist flexion) in a random order (Fig. 1a,b). We calibrated the initial neural network (NN) model using 40 sessions (80 blocks) from the training period (Fig. 1c). As the model was not updated at all over the subsequent test period, we call it the fixed NN (fNN). Two additional NN models were created from the fNN using updating procedures that used the first block of each of the 40 sessions in the testing period in different ways (Fig. 1c): supervised updating (sNN) or unsupervised updating (uNN) (Fig. 1d and Methods). In this context, supervised refers to the algorithm using explicit training labels (i.e., known timing and type of intended action) as opposed to unsupervised, in which the timing and type of intended action were unknown, as occurs with general BCI use. For comparison, the first block of each of the 40 sessions in the testing period was also used to calibrate benchmark BCI decoders that were retrained daily: a support vector machine (SVM) decoder (Fig. 1d) 8,23,25,26 , a linear discriminant analysis (LDA) decoder 17 , and a naive Bayes decoder 18. The SVM performed better than the LDA or naive Bayes decoder (Supplementary Fig. 1) and was thus used for further comparisons with NN performance. Neural features used by all models were the mean wavelet power (MWP) values calculated from raw voltage for each of the 96 channels over 100-ms bins 8,23,24 (Fig. 1e, Supplementary Fig. 2, and Methods). Performance for each of the NN and comparison models was initially evaluated using accuracy (percentage of correctly predicted time-bins) on the second block of data from each session during the testing period (Methods). Figure 1e shows data processing steps and NN model architecture. To quantify improved BCI accuracy with the NN, we compared the performance of the supervised, daily-updated sNN against a daily-retrained SVM. Figure 2a,b shows that the sNN was more accurate than the daily-retrained SVM for all sessions, with a mean difference of 6.35 ± 2.47% (mean ± s.d.; P = 3.69 × 20-8 , V = 820, n = 40 paired two-sided Wilcoxon signed rank test; n is the sample size and V is the test statistic for the paired Wilcoxon test). In addition, for 37 out of 40 sessions, the sNN accuracy was \u003e 90%, indicating consistently high performance in accordance with user expectations 13. In contrast, the SVM accuracy was \u003e 90% for only 12 sessions. To demonstrate that a BCI with a neural network decoder (NN-BCI) could sustain high accuracy for over a year without the need of supervised updating (thus reducing daily setup time), we evaluated performance of the fNN. Figure 2c shows that the fNN was more accurate than the daily-retrained SVM for 36 out of 40 test sessions, with a mean difference of 4.56. ± 3.06% (P = 1.90 × 10-7 , V = 798, n = 40; Fig. 2c, inset). In addition, the fNN accuracy was \u003e 90% for 32 sessions. Not only was the fNN able to sustain high accuracy decoding performance for over a year (381 d) without being recalibrated, it significantly outperformed all fixed versions of the benchmark decoders we tested (Supplementary Fig. 1). However, the fNN accuracy was lower than that of the sNN (Fig. 2d), which received supervised updates throughout the testing period. In fact,","internal_url":"https://www.academia.edu/40049262/Meeting_brain_computer_interface_user_performance_expectations_using_a_deep_neural_network_decoding_framework","translated_internal_url":"","created_at":"2019-08-09T14:59:20.279-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892977,"work_id":40049262,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":1,"name":"Marcia Bockbrader","title":"Meeting brain-computer interface user performance expectations using a deep neural network decoding framework"},{"id":32892978,"work_id":40049262,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":6885049,"email":"s***r@battelle.org","display_order":2,"name":"Michael Schwemmer","title":"Meeting brain-computer interface user performance 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class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049219/A_Characterization_of_Brain_Computer_Interface_Performance_Trade_Offs_Using_Support_Vector_Machines_and_Deep_Neural_Networks_to_Decode_Movement_Intent">A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent</a></div><div class="wp-workCard_item"><span>Frontiers in Neuroscience</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing dis...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. Surveys of potential users have revealed several key BCI performance criteria for clinical translation of such a system. Of these criteria, high accuracy, short response latencies, and multi-functionality are three key characteristics directly impacted by the neural decoding component of the BCI system, the algorithm that translates neural activity into control signals. Building a decoder that simultaneously addresses these three criteria is complicated because optimizing for one criterion may lead to undesirable changes in the other criteria. Unfortunately, there has been little work to date to quantify how decoder design simultaneously affects these performance characteristics. Here, we systematically explore the trade-off between accuracy, response latency, and multi-functionality for discrete movement classification using two different decoding strategies-a support vector machine (SVM) classifier which represents the current state-of-the-art for discrete movement classification in laboratory demonstrations and a proposed deep neural network (DNN) framework. We utilized historical intracortical recordings from a human tetraplegic study participant, who imagined performing several different hand and finger movements. For both decoders, we found that response time increases (i.e., slower reaction) and accuracy decreases as the number of functions increases. However, we also found that both the increase of response times and the decline in accuracy with additional functions is less for the DNN than the SVM. We also show that data preprocessing steps can affect the performance characteristics of the two decoders in drastically different ways. Finally, we evaluated the performance of our tetraplegic participant using the DNN decoder in real-time to control functional electrical stimulation (FES) of his paralyzed forearm. We compared his</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7bccb694ff8c6726904719ca3d6cd1f6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248689,"asset_id":40049219,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248689/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049219"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049219"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049219; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049219]").text(description); $(".js-view-count[data-work-id=40049219]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049219; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049219']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049219, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7bccb694ff8c6726904719ca3d6cd1f6" } } $('.js-work-strip[data-work-id=40049219]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049219,"title":"A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent","translated_title":"","metadata":{"doi":"10.3389/fnins.2018.00763","volume":"12","abstract":"Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. 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Building a decoder that simultaneously addresses these three criteria is complicated because optimizing for one criterion may lead to undesirable changes in the other criteria. Unfortunately, there has been little work to date to quantify how decoder design simultaneously affects these performance characteristics. Here, we systematically explore the trade-off between accuracy, response latency, and multi-functionality for discrete movement classification using two different decoding strategies-a support vector machine (SVM) classifier which represents the current state-of-the-art for discrete movement classification in laboratory demonstrations and a proposed deep neural network (DNN) framework. We utilized historical intracortical recordings from a human tetraplegic study participant, who imagined performing several different hand and finger movements. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049191"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049191/Beyond_Bones_Assessing_Whether_Ultrasound_Aided_Instruction_and_Practice_Improve_Unassisted_Soft_Tissue_Palpation_Skills_of_First_Year_Medical_Students"><img alt="Research paper thumbnail of Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students" class="work-thumbnail" src="https://attachments.academia-assets.com/60248667/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049191/Beyond_Bones_Assessing_Whether_Ultrasound_Aided_Instruction_and_Practice_Improve_Unassisted_Soft_Tissue_Palpation_Skills_of_First_Year_Medical_Students">Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://osu.academia.edu/MarcieBockbrader">Marcie Bockbrader</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://osu1.academia.edu/DavidWay">David Way</a></span></div><div class="wp-workCard_item"><span>Journal of Ultrasound in Medicine</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objectives-Our purpose was to determine whether ultrasound (US)-aided instruction and practice on...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objectives-Our purpose was to determine whether ultrasound (US)-aided instruction and practice on musculoskeletal anatomy would improve first-year medical students' ability to locate and identify specific soft tissue structures by unaided palpation in the upper and lower extremities of healthy human models. Methods-This study was a randomized crossover design with 49 first-year medical students randomly assigned to 1 of 2 groups. Each group was provided expert instruction and hands-on practice using US to scan and study soft tissue structures. During session 1, group A learned the anatomy of the upper extremities , whereas group B learned the lower. Students were then tested on their proficiency in locating 4 soft tissue structures (2 upper and 2 lower extremities) through palpation of a human model. During session 2, group A learned lower extremities, and group B learned upper. At the end of session 2, students repeated the assessment. Results-After the first instructional session, neither group performed significantly better on identifying and locating the soft tissue landmarks they learned aided by US. After the second instructional session, however, scores for both groups increased approximately 20 percentage points, indicating that both groups performed significantly better on palpating and identifying both the upper and lower extremity soft tissue landmarks (Cohen d = 0.89 and 0.82, respectively). Conclusions-Time and practice viewing soft tissue structures with US assistance seems to have a "palpation-with-eyes" effect that improves students' abilities to correctly locate, palpate, and identify limb-specific soft tissue structures once the US assistance is removed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6c36a35308f929ef1c2ef5666e58f804" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248667,"asset_id":40049191,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248667/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049191"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049191"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049191; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049191]").text(description); $(".js-view-count[data-work-id=40049191]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049191; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049191']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049191, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6c36a35308f929ef1c2ef5666e58f804" } } $('.js-work-strip[data-work-id=40049191]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049191,"title":"Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students","translated_title":"","metadata":{"doi":"10.1002/jum.14894","abstract":"Objectives-Our purpose was to determine whether ultrasound (US)-aided instruction and practice on musculoskeletal anatomy would improve first-year medical students' ability to locate and identify specific soft tissue structures by unaided palpation in the upper and lower extremities of healthy human models. Methods-This study was a randomized crossover design with 49 first-year medical students randomly assigned to 1 of 2 groups. Each group was provided expert instruction and hands-on practice using US to scan and study soft tissue structures. During session 1, group A learned the anatomy of the upper extremities , whereas group B learned the lower. Students were then tested on their proficiency in locating 4 soft tissue structures (2 upper and 2 lower extremities) through palpation of a human model. During session 2, group A learned lower extremities, and group B learned upper. At the end of session 2, students repeated the assessment. Results-After the first instructional session, neither group performed significantly better on identifying and locating the soft tissue landmarks they learned aided by US. After the second instructional session, however, scores for both groups increased approximately 20 percentage points, indicating that both groups performed significantly better on palpating and identifying both the upper and lower extremity soft tissue landmarks (Cohen d = 0.89 and 0.82, respectively). Conclusions-Time and practice viewing soft tissue structures with US assistance seems to have a \"palpation-with-eyes\" effect that improves students' abilities to correctly locate, palpate, and identify limb-specific soft tissue structures once the US assistance is removed.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Journal of Ultrasound in Medicine"},"translated_abstract":"Objectives-Our purpose was to determine whether ultrasound (US)-aided instruction and practice on musculoskeletal anatomy would improve first-year medical students' ability to locate and identify specific soft tissue structures by unaided palpation in the upper and lower extremities of healthy human models. Methods-This study was a randomized crossover design with 49 first-year medical students randomly assigned to 1 of 2 groups. Each group was provided expert instruction and hands-on practice using US to scan and study soft tissue structures. During session 1, group A learned the anatomy of the upper extremities , whereas group B learned the lower. Students were then tested on their proficiency in locating 4 soft tissue structures (2 upper and 2 lower extremities) through palpation of a human model. During session 2, group A learned lower extremities, and group B learned upper. At the end of session 2, students repeated the assessment. Results-After the first instructional session, neither group performed significantly better on identifying and locating the soft tissue landmarks they learned aided by US. After the second instructional session, however, scores for both groups increased approximately 20 percentage points, indicating that both groups performed significantly better on palpating and identifying both the upper and lower extremity soft tissue landmarks (Cohen d = 0.89 and 0.82, respectively). Conclusions-Time and practice viewing soft tissue structures with US assistance seems to have a \"palpation-with-eyes\" effect that improves students' abilities to correctly locate, palpate, and identify limb-specific soft tissue structures once the US assistance is removed.","internal_url":"https://www.academia.edu/40049191/Beyond_Bones_Assessing_Whether_Ultrasound_Aided_Instruction_and_Practice_Improve_Unassisted_Soft_Tissue_Palpation_Skills_of_First_Year_Medical_Students","translated_internal_url":"","created_at":"2019-08-09T14:43:25.876-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892933,"work_id":40049191,"tagging_user_id":7626683,"tagged_user_id":38450590,"co_author_invite_id":null,"email":"d***y@osumc.edu","affiliation":"The Ohio State University","display_order":1,"name":"David Way","title":"Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students"},{"id":32892934,"work_id":40049191,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":2,"name":"Marcia Bockbrader","title":"Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students"},{"id":32892935,"work_id":40049191,"tagging_user_id":7626683,"tagged_user_id":38233222,"co_author_invite_id":null,"email":"d***r@osumc.edu","display_order":3,"name":"David Bahner","title":"Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students"},{"id":32892936,"work_id":40049191,"tagging_user_id":7626683,"tagged_user_id":122859741,"co_author_invite_id":6885043,"email":"b***d@osumc.edu","display_order":4,"name":"Bryant Walrod","title":"Beyond Bones Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical 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Education","url":"https://www.academia.edu/Documents/in/Medical_Education"},{"id":163322,"name":"Graduate medical education","url":"https://www.academia.edu/Documents/in/Graduate_medical_education"},{"id":2350749,"name":"Musculoskelatal Ultrasound","url":"https://www.academia.edu/Documents/in/Musculoskelatal_Ultrasound"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049173"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049173/Clinically_Significant_Gains_in_Skillful_Grasp_Coordination_by_an_Individual_With_Tetraplegia_Using_an_Implanted_Brain_Computer_Interface_With_Forearm_Transcutaneous_Muscle_Stimulation"><img alt="Research paper thumbnail of Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation" class="work-thumbnail" src="https://attachments.academia-assets.com/60248646/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049173/Clinically_Significant_Gains_in_Skillful_Grasp_Coordination_by_an_Individual_With_Tetraplegia_Using_an_Implanted_Brain_Computer_Interface_With_Forearm_Transcutaneous_Muscle_Stimulation">Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation</a></div><div class="wp-workCard_item"><span>Archives of Physical Medicine & Rehabilitation</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objective: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objective: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic.<br />Design:Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days137 to 1478.Setting:Tertiary care outpatient rehabilitation center.Participant:A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injuryInterventions:After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordi-nated forearm, wrist, and hand movements.Main Outcome Measures:Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, andPrehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functionalactivity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Inde-pendence MeasureeSelf-Report [SCIM-SR]) with and without the BCI-FES.Results:With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can,fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-Twrist and handskills. QIF-SFand SCIM-SR eating, grooming, and toileting activities were expected to improvewith home use of BCI-FES. Pincer grips and mobilitywere unaffected. BCI-FES grip skills enabled the participant to play an adapted “Battleship” game and manipulate household objects.Conclusions:Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upperlimb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologiclevel gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4dd25dffa97e090f1b897d55cc1d9e9f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248646,"asset_id":40049173,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248646/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049173"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049173"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049173; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049173]").text(description); $(".js-view-count[data-work-id=40049173]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049173; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049173']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049173, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4dd25dffa97e090f1b897d55cc1d9e9f" } } $('.js-work-strip[data-work-id=40049173]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049173,"title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation","translated_title":"","metadata":{"doi":"10.1016/j.apmr.2018.07.445","abstract":"Objective: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic.\nDesign:Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days137 to 1478.Setting:Tertiary care outpatient rehabilitation center.Participant:A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injuryInterventions:After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordi-nated forearm, wrist, and hand movements.Main Outcome Measures:Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, andPrehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functionalactivity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Inde-pendence MeasureeSelf-Report [SCIM-SR]) with and without the BCI-FES.Results:With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can,fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-Twrist and handskills. QIF-SFand SCIM-SR eating, grooming, and toileting activities were expected to improvewith home use of BCI-FES. Pincer grips and mobilitywere unaffected. BCI-FES grip skills enabled the participant to play an adapted “Battleship” game and manipulate household objects.Conclusions:Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upperlimb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologiclevel gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Archives of Physical Medicine \u0026 Rehabilitation"},"translated_abstract":"Objective: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic.\nDesign:Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days137 to 1478.Setting:Tertiary care outpatient rehabilitation center.Participant:A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injuryInterventions:After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordi-nated forearm, wrist, and hand movements.Main Outcome Measures:Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, andPrehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functionalactivity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Inde-pendence MeasureeSelf-Report [SCIM-SR]) with and without the BCI-FES.Results:With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can,fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-Twrist and handskills. QIF-SFand SCIM-SR eating, grooming, and toileting activities were expected to improvewith home use of BCI-FES. Pincer grips and mobilitywere unaffected. BCI-FES grip skills enabled the participant to play an adapted “Battleship” game and manipulate household objects.Conclusions:Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upperlimb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologiclevel gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics.","internal_url":"https://www.academia.edu/40049173/Clinically_Significant_Gains_in_Skillful_Grasp_Coordination_by_an_Individual_With_Tetraplegia_Using_an_Implanted_Brain_Computer_Interface_With_Forearm_Transcutaneous_Muscle_Stimulation","translated_internal_url":"","created_at":"2019-08-09T14:39:11.298-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892927,"work_id":40049173,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":6327072,"email":"b***3@osu.edu","display_order":1,"name":"Marcie Bockbrader","title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation"},{"id":32892928,"work_id":40049173,"tagging_user_id":7626683,"tagged_user_id":47636515,"co_author_invite_id":null,"email":"f***d@battelle.org","display_order":2,"name":"David Friedenberg","title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation"},{"id":32892929,"work_id":40049173,"tagging_user_id":7626683,"tagged_user_id":43069448,"co_author_invite_id":null,"email":"s***s@osumc.edu","display_order":3,"name":"Sam Colachis","title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation"},{"id":32892930,"work_id":40049173,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":4404282,"email":"b***n@battelle.org","display_order":4,"name":"Chad Bouton","title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation"},{"id":32892931,"work_id":40049173,"tagging_user_id":7626683,"tagged_user_id":38974648,"co_author_invite_id":null,"email":"w***w@osumc.edu","display_order":5,"name":"W. Mysiw","title":"Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation"}],"downloadable_attachments":[{"id":60248646,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248646/thumbnails/1.jpg","file_name":"Bockbrader_etal2019_BCI_SCI_ClinicalGAIN20190809-13127-lk3orw.pdf","download_url":"https://www.academia.edu/attachments/60248646/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Clinically_Significant_Gains_in_Skillful.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248646/Bockbrader_etal2019_BCI_SCI_ClinicalGAIN20190809-13127-lk3orw-libre.pdf?1565388304=\u0026response-content-disposition=attachment%3B+filename%3DClinically_Significant_Gains_in_Skillful.pdf\u0026Expires=1732225873\u0026Signature=BCrQRnguZJMrSFXsZ9CMB1sajJwsYQIXvjbFLehsCpq~7C6GBSmqPDDKvcMUHTaJwGwCgNN3kUa6yY5RlMXqmnsdSQd3CTZJcuoHiYpIfyK0re~fVUCd01JSEyG3OpsJlGuk30YOy9c9m1N85~3ji8atcvxPn0tOlmMjBMvKRaZr8kiaZerim2YsGYH2DwJVMyuxoisfgPaD6NDz7fPKT2botjQcFy7AxkE5gOKQ71MGODFcxLaDc~cpSwKFTiO4RhnE94i6lvKkLdYGXjvN4ZvcsFUcx9e8-~M7kMhfI0cTQ4H1~cpRJS~ZyFvMcHqwub3fMmFGqXBnXMod8NhREQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Clinically_Significant_Gains_in_Skillful_Grasp_Coordination_by_an_Individual_With_Tetraplegia_Using_an_Implanted_Brain_Computer_Interface_With_Forearm_Transcutaneous_Muscle_Stimulation","translated_slug":"","page_count":17,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248646,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248646/thumbnails/1.jpg","file_name":"Bockbrader_etal2019_BCI_SCI_ClinicalGAIN20190809-13127-lk3orw.pdf","download_url":"https://www.academia.edu/attachments/60248646/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Clinically_Significant_Gains_in_Skillful.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248646/Bockbrader_etal2019_BCI_SCI_ClinicalGAIN20190809-13127-lk3orw-libre.pdf?1565388304=\u0026response-content-disposition=attachment%3B+filename%3DClinically_Significant_Gains_in_Skillful.pdf\u0026Expires=1732225873\u0026Signature=BCrQRnguZJMrSFXsZ9CMB1sajJwsYQIXvjbFLehsCpq~7C6GBSmqPDDKvcMUHTaJwGwCgNN3kUa6yY5RlMXqmnsdSQd3CTZJcuoHiYpIfyK0re~fVUCd01JSEyG3OpsJlGuk30YOy9c9m1N85~3ji8atcvxPn0tOlmMjBMvKRaZr8kiaZerim2YsGYH2DwJVMyuxoisfgPaD6NDz7fPKT2botjQcFy7AxkE5gOKQ71MGODFcxLaDc~cpSwKFTiO4RhnE94i6lvKkLdYGXjvN4ZvcsFUcx9e8-~M7kMhfI0cTQ4H1~cpRJS~ZyFvMcHqwub3fMmFGqXBnXMod8NhREQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":18450,"name":"Neuromuscular Control","url":"https://www.academia.edu/Documents/in/Neuromuscular_Control"},{"id":22824,"name":"Spinal Cord Injury","url":"https://www.academia.edu/Documents/in/Spinal_Cord_Injury"},{"id":46043,"name":"Functional Electrical Stimulation","url":"https://www.academia.edu/Documents/in/Functional_Electrical_Stimulation"},{"id":644153,"name":"Brain Computer Interface BCI","url":"https://www.academia.edu/Documents/in/Brain_Computer_Interface_BCI"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049137"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049137/Towards_a_consensus_for_musculoskeletal_ultrasonography_education_in_Physical_Medicine_and_Rehabilitation_A_national_poll_of_residency_directors"><img alt="Research paper thumbnail of Towards a consensus for musculoskeletal ultrasonography education in Physical Medicine & Rehabilitation: A national poll of residency directors" class="work-thumbnail" src="https://attachments.academia-assets.com/60248606/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049137/Towards_a_consensus_for_musculoskeletal_ultrasonography_education_in_Physical_Medicine_and_Rehabilitation_A_national_poll_of_residency_directors">Towards a consensus for musculoskeletal ultrasonography education in Physical Medicine & Rehabilitation: A national poll of residency directors</a></div><div class="wp-workCard_item"><span>American Journal of Physical Medicine & Rehabilitation</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objectives The aims of the study were to evaluate integration of musculoskeletal ultrasonography ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objectives The aims of the study were to evaluate integration of musculoskeletal ultrasonography education in physical medicine and rehabilitation training programs in 2014–2015, when the American Academy of Physical Medicine & Rehabilitation and Accreditation Council for Graduate Medical Education Residency Review Committee both recognized it as a fundamental component of physiatric practice, to identify common musculoskeletal ultrasonography components of physical medicine and rehabilitation residency curricula, and to identify common barriers to integration.<br /><br />Design Survey of 78 Accreditation Council for Graduate Medical Education–accredited physical medicine and rehabilitation residency programs was conducted.<br /><br />Results The 2015 survey response rate was more than 50%, and respondents were representative of programs across the United States. Most programs (80%) reported teaching musculoskeletal ultrasonography, whereas a minority (20%) required mastery of ultrasonography skills for graduation. Ultrasonography curricula varied, although most programs agreed that the scope of resident training in physical medicine and rehabilitation should include diagnostic and interventional musculoskeletal ultrasonography, especially for key joints (shoulder, elbow, knee, wrist, hip, and ankle) and nerves (median, ulnar, fibular, tibial, radial, and sciatic). Barriers to teaching included insufficient expertise of instructors, poor access to equipment, and lack of a structured curriculum.<br /><br />Conclusions Musculoskeletal ultrasonography has become a required component of physical medicine and rehabilitation residency training. Based on survey responses and expert recommendations, we propose a structure for musculoskeletal ultrasonography curricular standards and milestones for trainee competency.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bc223b79dc35812a5ac076250796e1cf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248606,"asset_id":40049137,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248606/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049137"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049137"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049137; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049137]").text(description); $(".js-view-count[data-work-id=40049137]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049137; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049137']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049137, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "bc223b79dc35812a5ac076250796e1cf" } } $('.js-work-strip[data-work-id=40049137]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049137,"title":"Towards a consensus for musculoskeletal ultrasonography education in Physical Medicine \u0026 Rehabilitation: A national poll of residency directors","translated_title":"","metadata":{"doi":"10.1097/PHM.0000000000001195","abstract":"Objectives The aims of the study were to evaluate integration of musculoskeletal ultrasonography education in physical medicine and rehabilitation training programs in 2014–2015, when the American Academy of Physical Medicine \u0026 Rehabilitation and Accreditation Council for Graduate Medical Education Residency Review Committee both recognized it as a fundamental component of physiatric practice, to identify common musculoskeletal ultrasonography components of physical medicine and rehabilitation residency curricula, and to identify common barriers to integration.\n\nDesign Survey of 78 Accreditation Council for Graduate Medical Education–accredited physical medicine and rehabilitation residency programs was conducted.\n\nResults The 2015 survey response rate was more than 50%, and respondents were representative of programs across the United States. Most programs (80%) reported teaching musculoskeletal ultrasonography, whereas a minority (20%) required mastery of ultrasonography skills for graduation. Ultrasonography curricula varied, although most programs agreed that the scope of resident training in physical medicine and rehabilitation should include diagnostic and interventional musculoskeletal ultrasonography, especially for key joints (shoulder, elbow, knee, wrist, hip, and ankle) and nerves (median, ulnar, fibular, tibial, radial, and sciatic). Barriers to teaching included insufficient expertise of instructors, poor access to equipment, and lack of a structured curriculum.\n\nConclusions Musculoskeletal ultrasonography has become a required component of physical medicine and rehabilitation residency training. Based on survey responses and expert recommendations, we propose a structure for musculoskeletal ultrasonography curricular standards and milestones for trainee competency.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"American Journal of Physical Medicine \u0026 Rehabilitation"},"translated_abstract":"Objectives The aims of the study were to evaluate integration of musculoskeletal ultrasonography education in physical medicine and rehabilitation training programs in 2014–2015, when the American Academy of Physical Medicine \u0026 Rehabilitation and Accreditation Council for Graduate Medical Education Residency Review Committee both recognized it as a fundamental component of physiatric practice, to identify common musculoskeletal ultrasonography components of physical medicine and rehabilitation residency curricula, and to identify common barriers to integration.\n\nDesign Survey of 78 Accreditation Council for Graduate Medical Education–accredited physical medicine and rehabilitation residency programs was conducted.\n\nResults The 2015 survey response rate was more than 50%, and respondents were representative of programs across the United States. Most programs (80%) reported teaching musculoskeletal ultrasonography, whereas a minority (20%) required mastery of ultrasonography skills for graduation. Ultrasonography curricula varied, although most programs agreed that the scope of resident training in physical medicine and rehabilitation should include diagnostic and interventional musculoskeletal ultrasonography, especially for key joints (shoulder, elbow, knee, wrist, hip, and ankle) and nerves (median, ulnar, fibular, tibial, radial, and sciatic). Barriers to teaching included insufficient expertise of instructors, poor access to equipment, and lack of a structured curriculum.\n\nConclusions Musculoskeletal ultrasonography has become a required component of physical medicine and rehabilitation residency training. Based on survey responses and expert recommendations, we propose a structure for musculoskeletal ultrasonography curricular standards and milestones for trainee competency.","internal_url":"https://www.academia.edu/40049137/Towards_a_consensus_for_musculoskeletal_ultrasonography_education_in_Physical_Medicine_and_Rehabilitation_A_national_poll_of_residency_directors","translated_internal_url":"","created_at":"2019-08-09T14:26:59.320-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":60248606,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248606/thumbnails/1.jpg","file_name":"Bockbrader_etal2018_MSKUS_PMR20190809-12728-lq0rgm.pdf","download_url":"https://www.academia.edu/attachments/60248606/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Towards_a_consensus_for_musculoskeletal.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248606/Bockbrader_etal2018_MSKUS_PMR20190809-12728-lq0rgm-libre.pdf?1565388314=\u0026response-content-disposition=attachment%3B+filename%3DTowards_a_consensus_for_musculoskeletal.pdf\u0026Expires=1732437019\u0026Signature=PfDi56ZJ6ANbgVPkTbUbiJgpPNJkg3TI492JTPXkop72j6XGRwu1BnLJ1A7z~1Se8zI99x6FOsTo552qKeNn87lM-zuI7X6tnN8SgbZZWuom0g78DOPfsuPLFImXYrG08dyxrq5nI1W5~QDFnFG7k3Y5byzgHF8a7t32HNkQ3LolDEI7H77x4EO2R8~U~Z9zVwXUW0kEzo37fb4zreKqKQ00Q6Vx2G3iAzYI2cJPnDcJcBjNbEqFPlXvpIRzXQegbn2x0hV8-JXlIoJVinQwVu6YSBNWFg-aFeBKnxBmVWOKOP7-nGlGHvK7lkCQMOIqQSr9M~mZWWzMevZbHsct-w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Towards_a_consensus_for_musculoskeletal_ultrasonography_education_in_Physical_Medicine_and_Rehabilitation_A_national_poll_of_residency_directors","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248606,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248606/thumbnails/1.jpg","file_name":"Bockbrader_etal2018_MSKUS_PMR20190809-12728-lq0rgm.pdf","download_url":"https://www.academia.edu/attachments/60248606/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Towards_a_consensus_for_musculoskeletal.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248606/Bockbrader_etal2018_MSKUS_PMR20190809-12728-lq0rgm-libre.pdf?1565388314=\u0026response-content-disposition=attachment%3B+filename%3DTowards_a_consensus_for_musculoskeletal.pdf\u0026Expires=1732437019\u0026Signature=PfDi56ZJ6ANbgVPkTbUbiJgpPNJkg3TI492JTPXkop72j6XGRwu1BnLJ1A7z~1Se8zI99x6FOsTo552qKeNn87lM-zuI7X6tnN8SgbZZWuom0g78DOPfsuPLFImXYrG08dyxrq5nI1W5~QDFnFG7k3Y5byzgHF8a7t32HNkQ3LolDEI7H77x4EO2R8~U~Z9zVwXUW0kEzo37fb4zreKqKQ00Q6Vx2G3iAzYI2cJPnDcJcBjNbEqFPlXvpIRzXQegbn2x0hV8-JXlIoJVinQwVu6YSBNWFg-aFeBKnxBmVWOKOP7-nGlGHvK7lkCQMOIqQSr9M~mZWWzMevZbHsct-w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":4455,"name":"Medical Education","url":"https://www.academia.edu/Documents/in/Medical_Education"},{"id":32001,"name":"Physical Medicine and Rehabilitation","url":"https://www.academia.edu/Documents/in/Physical_Medicine_and_Rehabilitation"},{"id":119911,"name":"Musculoskeletal Ultrasound","url":"https://www.academia.edu/Documents/in/Musculoskeletal_Ultrasound"},{"id":163322,"name":"Graduate medical education","url":"https://www.academia.edu/Documents/in/Graduate_medical_education"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40049105"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40049105/A_High_Definition_Non_invasive_Neuromuscular_Electrical_Stimulation_System_for_Cortical_Control_of_Combinatorial_Rotary_Hand_Movements_in_a_Human_with_Tetraplegia"><img alt="Research paper thumbnail of A High Definition Non-invasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human with Tetraplegia" class="work-thumbnail" src="https://attachments.academia-assets.com/60248573/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40049105/A_High_Definition_Non_invasive_Neuromuscular_Electrical_Stimulation_System_for_Cortical_Control_of_Combinatorial_Rotary_Hand_Movements_in_a_Human_with_Tetraplegia">A High Definition Non-invasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human with Tetraplegia</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Biomedical Engineering</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objective: Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on mul...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objective: Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on multiple arm and hand motor functions. Rotary hand movements, such as supination and pronation, are commonly impaired by upper extremity paralysis, and are essential for many activities of daily living. In this proof-of-concept study, we utilize a neural bypass system (NBS) to decode motor intention from motor cortex to control combinatorial rotary hand movements elicited through stimulation of the arm muscles, effectively bypassing the SCI of the study participant. We describe the NBS system architecture and design that enabled this functionality. Methods: The NBS consists of three main functional components: 1) implanted intracortical microelectrode array, 2) neural data processing using a computer, and, 3) a non-invasive neuromuscular electrical stimulation (NMES) system. Results: We address previous limitations of the NBS, and confirm the enhanced capability of the NBS to enable, in real-time, combinatorial hand rotary motor functions during a functionally relevant object manipulation task. Conclusion: This enhanced capability was enabled by accurate decoding of multiple movement intentions from the participant's motor cortex, interleaving NMES patterns to combine hand movements, and dynamically switching between NMES patterns to adjust for hand position changes during movement. Significance: These results have implications for enabling complex rotary hand functions in sequence with other</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a40b3cce90d15d2ee4a4f02cb9e614cc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60248573,"asset_id":40049105,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60248573/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40049105"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40049105"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40049105; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40049105]").text(description); $(".js-view-count[data-work-id=40049105]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40049105; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40049105']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40049105, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a40b3cce90d15d2ee4a4f02cb9e614cc" } } $('.js-work-strip[data-work-id=40049105]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40049105,"title":"A High Definition Non-invasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human with Tetraplegia","translated_title":"","metadata":{"doi":"10.1109/TBME.2018.2864104","abstract":"Objective: Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on multiple arm and hand motor functions. Rotary hand movements, such as supination and pronation, are commonly impaired by upper extremity paralysis, and are essential for many activities of daily living. In this proof-of-concept study, we utilize a neural bypass system (NBS) to decode motor intention from motor cortex to control combinatorial rotary hand movements elicited through stimulation of the arm muscles, effectively bypassing the SCI of the study participant. We describe the NBS system architecture and design that enabled this functionality. Methods: The NBS consists of three main functional components: 1) implanted intracortical microelectrode array, 2) neural data processing using a computer, and, 3) a non-invasive neuromuscular electrical stimulation (NMES) system. Results: We address previous limitations of the NBS, and confirm the enhanced capability of the NBS to enable, in real-time, combinatorial hand rotary motor functions during a functionally relevant object manipulation task. Conclusion: This enhanced capability was enabled by accurate decoding of multiple movement intentions from the participant's motor cortex, interleaving NMES patterns to combine hand movements, and dynamically switching between NMES patterns to adjust for hand position changes during movement. Significance: These results have implications for enabling complex rotary hand functions in sequence with other","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"IEEE Transactions on Biomedical Engineering"},"translated_abstract":"Objective: Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on multiple arm and hand motor functions. Rotary hand movements, such as supination and pronation, are commonly impaired by upper extremity paralysis, and are essential for many activities of daily living. In this proof-of-concept study, we utilize a neural bypass system (NBS) to decode motor intention from motor cortex to control combinatorial rotary hand movements elicited through stimulation of the arm muscles, effectively bypassing the SCI of the study participant. We describe the NBS system architecture and design that enabled this functionality. Methods: The NBS consists of three main functional components: 1) implanted intracortical microelectrode array, 2) neural data processing using a computer, and, 3) a non-invasive neuromuscular electrical stimulation (NMES) system. Results: We address previous limitations of the NBS, and confirm the enhanced capability of the NBS to enable, in real-time, combinatorial hand rotary motor functions during a functionally relevant object manipulation task. Conclusion: This enhanced capability was enabled by accurate decoding of multiple movement intentions from the participant's motor cortex, interleaving NMES patterns to combine hand movements, and dynamically switching between NMES patterns to adjust for hand position changes during movement. Significance: These results have implications for enabling complex rotary hand functions in sequence with other","internal_url":"https://www.academia.edu/40049105/A_High_Definition_Non_invasive_Neuromuscular_Electrical_Stimulation_System_for_Cortical_Control_of_Combinatorial_Rotary_Hand_Movements_in_a_Human_with_Tetraplegia","translated_internal_url":"","created_at":"2019-08-09T14:19:16.239-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32892908,"work_id":40049105,"tagging_user_id":7626683,"tagged_user_id":47841492,"co_author_invite_id":null,"email":"a***n@battelle.org","display_order":1,"name":"Nicholas Annetta","title":"A High Definition Non-invasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human with Tetraplegia"}],"downloadable_attachments":[{"id":60248573,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248573/thumbnails/1.jpg","file_name":"Annetta_etal2018_BCIFES_rotary20190809-26013-24xrud.pdf","download_url":"https://www.academia.edu/attachments/60248573/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_High_Definition_Non_invasive_Neuromusc.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248573/Annetta_etal2018_BCIFES_rotary20190809-26013-24xrud-libre.pdf?1565388315=\u0026response-content-disposition=attachment%3B+filename%3DA_High_Definition_Non_invasive_Neuromusc.pdf\u0026Expires=1732437019\u0026Signature=GelTQCPUIQiAFZOgmV9Ap4q0hLeGwer61pdslJQr4aJCRvBbbn3M8NyytH6RsK5j9tv8GkKfReRPtfGqcGGC5BV3pr5mnkqTKbMI870cwZfCuXe1R4ncm4VaZQZ-Ibp12C0rzSqcuKmOUIQ~YG-a6qM24AMibjM1HF5kbV0kMqrIj3Uj9XukKYIJVMNMHLHTNVqnBIm1dyna5a7jsc5Nd2DkGzoxoHkCg8CorlYXzHFN2g8orJHU9FT1gdiVCSLSHXzbEIpMO1UIhgh2Ovd9Rm1HO5AChpfIrGnSe~4y444vuWhpt~48BOV46tFx7Dpn8SVXQeUeTWImnvLEpXAbOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_High_Definition_Non_invasive_Neuromuscular_Electrical_Stimulation_System_for_Cortical_Control_of_Combinatorial_Rotary_Hand_Movements_in_a_Human_with_Tetraplegia","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":60248573,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60248573/thumbnails/1.jpg","file_name":"Annetta_etal2018_BCIFES_rotary20190809-26013-24xrud.pdf","download_url":"https://www.academia.edu/attachments/60248573/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_High_Definition_Non_invasive_Neuromusc.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60248573/Annetta_etal2018_BCIFES_rotary20190809-26013-24xrud-libre.pdf?1565388315=\u0026response-content-disposition=attachment%3B+filename%3DA_High_Definition_Non_invasive_Neuromusc.pdf\u0026Expires=1732437019\u0026Signature=GelTQCPUIQiAFZOgmV9Ap4q0hLeGwer61pdslJQr4aJCRvBbbn3M8NyytH6RsK5j9tv8GkKfReRPtfGqcGGC5BV3pr5mnkqTKbMI870cwZfCuXe1R4ncm4VaZQZ-Ibp12C0rzSqcuKmOUIQ~YG-a6qM24AMibjM1HF5kbV0kMqrIj3Uj9XukKYIJVMNMHLHTNVqnBIm1dyna5a7jsc5Nd2DkGzoxoHkCg8CorlYXzHFN2g8orJHU9FT1gdiVCSLSHXzbEIpMO1UIhgh2Ovd9Rm1HO5AChpfIrGnSe~4y444vuWhpt~48BOV46tFx7Dpn8SVXQeUeTWImnvLEpXAbOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":18450,"name":"Neuromuscular Control","url":"https://www.academia.edu/Documents/in/Neuromuscular_Control"},{"id":22824,"name":"Spinal Cord Injury","url":"https://www.academia.edu/Documents/in/Spinal_Cord_Injury"},{"id":46043,"name":"Functional Electrical Stimulation","url":"https://www.academia.edu/Documents/in/Functional_Electrical_Stimulation"},{"id":644153,"name":"Brain Computer Interface BCI","url":"https://www.academia.edu/Documents/in/Brain_Computer_Interface_BCI"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="39401710"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/39401710/Randomized_Sham_Controlled_Trial_of_Navigated_Repetitive_Transcranial_Magnetic_Stimulation_for_Motor_Recovery_in_Stroke_The_NICHE_Trial"><img alt="Research paper thumbnail of Randomized Sham-Controlled Trial of Navigated Repetitive Transcranial Magnetic Stimulation for Motor Recovery in Stroke: The NICHE Trial" class="work-thumbnail" src="https://attachments.academia-assets.com/59545749/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/39401710/Randomized_Sham_Controlled_Trial_of_Navigated_Repetitive_Transcranial_Magnetic_Stimulation_for_Motor_Recovery_in_Stroke_The_NICHE_Trial">Randomized Sham-Controlled Trial of Navigated Repetitive Transcranial Magnetic Stimulation for Motor Recovery in Stroke: The NICHE Trial</a></div><div class="wp-workCard_item"><span>Stroke</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background and Purpose―We aimed to determine whether low-frequency electric field navigated repet...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background and Purpose―We aimed to determine whether low-frequency electric field navigated repetitive transcranial<br />magnetic stimulation to noninjured motor cortex versus sham repetitive transcranial magnetic stimulation avoiding motor cortex could improve arm motor function in hemiplegic stroke patients when combined with motor training.<br />Methods―Twelve outpatient US rehabilitation centers enrolled participants between May 2014 and December 2015. We delivered 1 Hz active or sham repetitive transcranial magnetic stimulation to noninjured motor cortex before each of eighteen 60-minute therapy sessions over a 6-week period, with outcomes measured at 1 week and 1, 3, and 6 months after end of treatment. The primary end point was the percentage of participants improving ≥5 points on upper extremity Fugl-Meyer score 6 months after end of treatment. Secondary analyses assessed changes on the upper extremity Fugl-Meyer and Action Research Arm Test and Wolf Motor Function Test and safety.<br />Results―Of 199 participants, 167 completed treatment and follow-up because of early discontinuation of data collection. Upper extremity Fugl-Meyer gains were significant for experimental (P<0.001) and sham groups (P<0.001). Sixty-seven percent of the experimental group (95% CI, 58%–75%) and 65% of sham group (95% CI, 52%–76%) improved ≥5 points on 6-month upper extremity Fugl-Meyer (P=0.76). There was also no difference between experimental and sham groups in the Action Research Arm Test (P=0.80) or the Wolf Motor Function Test (P=0.55). A total of 26 serious adverse events occurred in 18 participants, with none related to the study or device, and with no difference between groups.<br />Conclusions―Among patients 3 to 12 months poststroke, goal-oriented motor rehabilitation improved motor function 6<br />months after end of treatment. There was no difference between the active and sham repetitive transcranial magnetic stimulation trial arms.<br />Clinical Trial Registration―URL: <a href="https://www.clinicaltrials.gov" rel="nofollow">https://www.clinicaltrials.gov</a>. Unique identifier: NCT02089464.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="62e99481b2ff4a3b5d0ee8a2f840dd4f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":59545749,"asset_id":39401710,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/59545749/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQxOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="39401710"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="39401710"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 39401710; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=39401710]").text(description); $(".js-view-count[data-work-id=39401710]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 39401710; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='39401710']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 39401710, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "62e99481b2ff4a3b5d0ee8a2f840dd4f" } } $('.js-work-strip[data-work-id=39401710]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":39401710,"title":"Randomized Sham-Controlled Trial of Navigated Repetitive Transcranial Magnetic Stimulation for Motor Recovery in Stroke: The NICHE Trial","translated_title":"","metadata":{"doi":"10.1161/STROKEAHA.117.020607","issue":"9","volume":"49","abstract":"Background and Purpose―We aimed to determine whether low-frequency electric field navigated repetitive transcranial\nmagnetic stimulation to noninjured motor cortex versus sham repetitive transcranial magnetic stimulation avoiding motor cortex could improve arm motor function in hemiplegic stroke patients when combined with motor training.\nMethods―Twelve outpatient US rehabilitation centers enrolled participants between May 2014 and December 2015. We delivered 1 Hz active or sham repetitive transcranial magnetic stimulation to noninjured motor cortex before each of eighteen 60-minute therapy sessions over a 6-week period, with outcomes measured at 1 week and 1, 3, and 6 months after end of treatment. The primary end point was the percentage of participants improving ≥5 points on upper extremity Fugl-Meyer score 6 months after end of treatment. Secondary analyses assessed changes on the upper extremity Fugl-Meyer and Action Research Arm Test and Wolf Motor Function Test and safety.\nResults―Of 199 participants, 167 completed treatment and follow-up because of early discontinuation of data collection. Upper extremity Fugl-Meyer gains were significant for experimental (P\u003c0.001) and sham groups (P\u003c0.001). Sixty-seven percent of the experimental group (95% CI, 58%–75%) and 65% of sham group (95% CI, 52%–76%) improved ≥5 points on 6-month upper extremity Fugl-Meyer (P=0.76). There was also no difference between experimental and sham groups in the Action Research Arm Test (P=0.80) or the Wolf Motor Function Test (P=0.55). A total of 26 serious adverse events occurred in 18 participants, with none related to the study or device, and with no difference between groups.\nConclusions―Among patients 3 to 12 months poststroke, goal-oriented motor rehabilitation improved motor function 6\nmonths after end of treatment. There was no difference between the active and sham repetitive transcranial magnetic stimulation trial arms.\nClinical Trial Registration―URL: https://www.clinicaltrials.gov. Unique identifier: NCT02089464.","page_numbers":"2138-46","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Stroke"},"translated_abstract":"Background and Purpose―We aimed to determine whether low-frequency electric field navigated repetitive transcranial\nmagnetic stimulation to noninjured motor cortex versus sham repetitive transcranial magnetic stimulation avoiding motor cortex could improve arm motor function in hemiplegic stroke patients when combined with motor training.\nMethods―Twelve outpatient US rehabilitation centers enrolled participants between May 2014 and December 2015. We delivered 1 Hz active or sham repetitive transcranial magnetic stimulation to noninjured motor cortex before each of eighteen 60-minute therapy sessions over a 6-week period, with outcomes measured at 1 week and 1, 3, and 6 months after end of treatment. The primary end point was the percentage of participants improving ≥5 points on upper extremity Fugl-Meyer score 6 months after end of treatment. Secondary analyses assessed changes on the upper extremity Fugl-Meyer and Action Research Arm Test and Wolf Motor Function Test and safety.\nResults―Of 199 participants, 167 completed treatment and follow-up because of early discontinuation of data collection. Upper extremity Fugl-Meyer gains were significant for experimental (P\u003c0.001) and sham groups (P\u003c0.001). Sixty-seven percent of the experimental group (95% CI, 58%–75%) and 65% of sham group (95% CI, 52%–76%) improved ≥5 points on 6-month upper extremity Fugl-Meyer (P=0.76). There was also no difference between experimental and sham groups in the Action Research Arm Test (P=0.80) or the Wolf Motor Function Test (P=0.55). A total of 26 serious adverse events occurred in 18 participants, with none related to the study or device, and with no difference between groups.\nConclusions―Among patients 3 to 12 months poststroke, goal-oriented motor rehabilitation improved motor function 6\nmonths after end of treatment. There was no difference between the active and sham repetitive transcranial magnetic stimulation trial arms.\nClinical Trial Registration―URL: https://www.clinicaltrials.gov. 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As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="db9043f6a7c30d78ab0378cdb286df3b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53275768,"asset_id":33194516,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53275768/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="33194516"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="33194516"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33194516; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33194516]").text(description); $(".js-view-count[data-work-id=33194516]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33194516; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='33194516']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 33194516, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "db9043f6a7c30d78ab0378cdb286df3b" } } $('.js-work-strip[data-work-id=33194516]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":33194516,"title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface","translated_title":"","metadata":{"abstract":"— Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI."},"translated_abstract":"— Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. 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Mysiw","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"},{"id":29098185,"work_id":33194516,"tagging_user_id":7626683,"tagged_user_id":29925460,"co_author_invite_id":null,"email":"f***2@aol.com","affiliation":"Yeshiva University","display_order":5,"name":"David Friedenberg","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"},{"id":29098186,"work_id":33194516,"tagging_user_id":7626683,"tagged_user_id":47945078,"co_author_invite_id":null,"email":"m***2@gmail.com","display_order":6,"name":"mingming zhang","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"},{"id":29098187,"work_id":33194516,"tagging_user_id":7626683,"tagged_user_id":32735859,"co_author_invite_id":null,"email":"m***r@gmail.com","affiliation":"Ohio State University","display_order":7,"name":"Michael Schwemmer","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"},{"id":29098188,"work_id":33194516,"tagging_user_id":7626683,"tagged_user_id":47841492,"co_author_invite_id":null,"email":"a***n@battelle.org","display_order":8,"name":"Nicholas Annetta","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"},{"id":29098189,"work_id":33194516,"tagging_user_id":7626683,"tagged_user_id":3973077,"co_author_invite_id":null,"email":"f***5@yahoo.com","display_order":9,"name":"Ali Rezai","title":"Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface"}],"downloadable_attachments":[{"id":53275768,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53275768/thumbnails/1.jpg","file_name":"Friedenberg_etal2016_Big_Data_Challenges_in_Decoding_Human_Brain_Data_to_Inform_a_Brain_Compu....pdf","download_url":"https://www.academia.edu/attachments/53275768/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Big_data_challenges_in_decoding_cortical.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53275768/Friedenberg_etal2016_Big_Data_Challenges_in_Decoding_Human_Brain_Data_to_Inform_a_Brain_Compu...-libre.pdf?1495726952=\u0026response-content-disposition=attachment%3B+filename%3DBig_data_challenges_in_decoding_cortical.pdf\u0026Expires=1732437020\u0026Signature=QjOiJcn1mRXOlOCb-FjJ5Q-KDVot2hPkqJAMUpzV~jZJ-VpZunVmczEGEcZXqkbc0OQ0BFXGbvhPcPwEO-z9Z1biMdlraBFOqwotMiMgnbZeaOmgFsvxxgE638MYpFgUimTqb4rux3uklksPwIeO3nG1ldruttk6UfA0ZBu55MGsAfz1VMQy25exz7M3ggJKdHaQUOZQqzuEHTvVAk2hX0A577iSSjYWwptlyDiderxP7M7sNfWKf2i2lUTypT3Z~mKS5xaiI-QYN6ftm-lB7FJKyetLcyRa5d77Wxh46tHcHrtbl91R9Z7hHuhbes9M0NM4~nSkUHzAD1CHnWFnRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Big_data_challenges_in_decoding_cortical_activity_in_a_human_with_quadriplegia_to_inform_a_brain_computer_interface","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":7626683,"first_name":"Marcie","middle_initials":null,"last_name":"Bockbrader","page_name":"MarcieBockbrader","domain_name":"osu","created_at":"2013-12-16T02:14:51.049-08:00","display_name":"Marcie Bockbrader","url":"https://osu.academia.edu/MarcieBockbrader"},"attachments":[{"id":53275768,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53275768/thumbnails/1.jpg","file_name":"Friedenberg_etal2016_Big_Data_Challenges_in_Decoding_Human_Brain_Data_to_Inform_a_Brain_Compu....pdf","download_url":"https://www.academia.edu/attachments/53275768/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Big_data_challenges_in_decoding_cortical.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53275768/Friedenberg_etal2016_Big_Data_Challenges_in_Decoding_Human_Brain_Data_to_Inform_a_Brain_Compu...-libre.pdf?1495726952=\u0026response-content-disposition=attachment%3B+filename%3DBig_data_challenges_in_decoding_cortical.pdf\u0026Expires=1732437020\u0026Signature=QjOiJcn1mRXOlOCb-FjJ5Q-KDVot2hPkqJAMUpzV~jZJ-VpZunVmczEGEcZXqkbc0OQ0BFXGbvhPcPwEO-z9Z1biMdlraBFOqwotMiMgnbZeaOmgFsvxxgE638MYpFgUimTqb4rux3uklksPwIeO3nG1ldruttk6UfA0ZBu55MGsAfz1VMQy25exz7M3ggJKdHaQUOZQqzuEHTvVAk2hX0A577iSSjYWwptlyDiderxP7M7sNfWKf2i2lUTypT3Z~mKS5xaiI-QYN6ftm-lB7FJKyetLcyRa5d77Wxh46tHcHrtbl91R9Z7hHuhbes9M0NM4~nSkUHzAD1CHnWFnRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":47558,"name":"BCI","url":"https://www.academia.edu/Documents/in/BCI"},{"id":786962,"name":"BCI rehabilitation","url":"https://www.academia.edu/Documents/in/BCI_rehabilitation"}],"urls":[]}, dispatcherData: dispatcherData }); 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Previous studies have shown reduced SSVEPs to alpha and lower frequencies of stimulation in schizophrenia. We investigated SSVEPs in schizophrenia at frequencies spanning the theta (4 Hz) to gamma (40 Hz) range. Methods: The SSVEPs to seven different frequencies of stimulation (4, 8, 17, 20, 23, 30 and 40 Hz) were obtained from 18 schizophrenia subjects and 33 healthy control subjects. Power at stimulating frequency (signal power) and power at frequencies above and below the stimulating frequency (noise power) were used to quantify the SSVEP responses. Results: Both groups showed an inverse relationship between power and frequency of stimulation. Schizophrenia subjects showed reduced signal power compared to healthy control subjects at higher frequencies (above 17 Hz), but not at 4 and 8 Hz at occipital region. Noise power was higher in schizophrenia subjects at frequencies between 4 and 20 Hz over occipital region and at 4, 17 and 20 Hz over frontal region. Conclusions: SSVEP signal power at beta and gamma frequencies of stimulation were reduced in schizophrenia. Schizophrenia subjects showed higher levels of EEG noise during photic stimulation at beta and lower frequencies. Significance: Inability to generate or maintain oscillations in neural networks may contribute to deficits in visual processing in schizophrenia.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="19feb859e9828bce98d028205bb17092" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53275796,"asset_id":33194515,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53275796/download_file?st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&st=MTczMjQzMzQyMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="33194515"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="33194515"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33194515; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33194515]").text(description); $(".js-view-count[data-work-id=33194515]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33194515; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='33194515']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 33194515, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "19feb859e9828bce98d028205bb17092" } } $('.js-work-strip[data-work-id=33194515]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":33194515,"title":"Steady state visual evoked potential abnormalities in schizophrenia","translated_title":"","metadata":{"abstract":"Objective: The steady state visual evoked potential (SSVEP) can be used to test the frequency response function of neural circuits. Previous studies have shown reduced SSVEPs to alpha and lower frequencies of stimulation in schizophrenia. We investigated SSVEPs in schizophrenia at frequencies spanning the theta (4 Hz) to gamma (40 Hz) range. Methods: The SSVEPs to seven different frequencies of stimulation (4, 8, 17, 20, 23, 30 and 40 Hz) were obtained from 18 schizophrenia subjects and 33 healthy control subjects. Power at stimulating frequency (signal power) and power at frequencies above and below the stimulating frequency (noise power) were used to quantify the SSVEP responses. Results: Both groups showed an inverse relationship between power and frequency of stimulation. Schizophrenia subjects showed reduced signal power compared to healthy control subjects at higher frequencies (above 17 Hz), but not at 4 and 8 Hz at occipital region. Noise power was higher in schizophrenia subjects at frequencies between 4 and 20 Hz over occipital region and at 4, 17 and 20 Hz over frontal region. Conclusions: SSVEP signal power at beta and gamma frequencies of stimulation were reduced in schizophrenia. Schizophrenia subjects showed higher levels of EEG noise during photic stimulation at beta and lower frequencies. Significance: Inability to generate or maintain oscillations in neural networks may contribute to deficits in visual processing in schizophrenia."},"translated_abstract":"Objective: The steady state visual evoked potential (SSVEP) can be used to test the frequency response function of neural circuits. Previous studies have shown reduced SSVEPs to alpha and lower frequencies of stimulation in schizophrenia. We investigated SSVEPs in schizophrenia at frequencies spanning the theta (4 Hz) to gamma (40 Hz) range. Methods: The SSVEPs to seven different frequencies of stimulation (4, 8, 17, 20, 23, 30 and 40 Hz) were obtained from 18 schizophrenia subjects and 33 healthy control subjects. Power at stimulating frequency (signal power) and power at frequencies above and below the stimulating frequency (noise power) were used to quantify the SSVEP responses. Results: Both groups showed an inverse relationship between power and frequency of stimulation. Schizophrenia subjects showed reduced signal power compared to healthy control subjects at higher frequencies (above 17 Hz), but not at 4 and 8 Hz at occipital region. Noise power was higher in schizophrenia subjects at frequencies between 4 and 20 Hz over occipital region and at 4, 17 and 20 Hz over frontal region. Conclusions: SSVEP signal power at beta and gamma frequencies of stimulation were reduced in schizophrenia. Schizophrenia subjects showed higher levels of EEG noise during photic stimulation at beta and lower frequencies. Significance: Inability to generate or maintain oscillations in neural networks may contribute to deficits in visual processing in schizophrenia.","internal_url":"https://www.academia.edu/33194515/Steady_state_visual_evoked_potential_abnormalities_in_schizophrenia","translated_internal_url":"","created_at":"2017-05-25T08:26:44.649-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7626683,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":29098236,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":43010706,"co_author_invite_id":null,"email":"a***r@iupui.edu","display_order":1,"name":"Anantha Shekhar","title":"Steady state visual evoked potential abnormalities in schizophrenia"},{"id":29098237,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":38561495,"co_author_invite_id":null,"email":"m***r@osumc.edu","affiliation":"The Ohio State University","display_order":2,"name":"Marcia Bockbrader","title":"Steady state visual evoked potential abnormalities in schizophrenia"},{"id":29098238,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":28831504,"co_author_invite_id":null,"email":"w***k@indiana.edu","display_order":3,"name":"William Hetrick","title":"Steady state visual evoked potential abnormalities in schizophrenia"},{"id":29098239,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":37636881,"co_author_invite_id":null,"email":"j***s@iupui.edu","affiliation":"Indiana University","display_order":4,"name":"Jenifer Vohs","title":"Steady state visual evoked potential abnormalities in schizophrenia"},{"id":29098240,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":null,"co_author_invite_id":1959492,"email":"g***n@indiana.edu","display_order":5,"name":"Giri Krishnan","title":"Steady state visual evoked potential abnormalities in schizophrenia"},{"id":29098241,"work_id":33194515,"tagging_user_id":7626683,"tagged_user_id":32793861,"co_author_invite_id":null,"email":"b***l@gmail.com","display_order":6,"name":"B. 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