Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals

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electroencephalography; lower-limb rehabilitation; motor tasks decoding; pedaling actual movement" xml:lang="en" /> <meta name="DC.description" content="8" /> <meta name="DC.type" content="Esperti anonimi" xml:lang="en" /> <meta name="DCTERMS.dateAccepted" content="2025-02-18T17:44:43Z" scheme="DCTERMS.W3CDTF" /> <meta name="DC.description" content="si" xml:lang="en" /> <meta name="DC.identifier" content="2-s2.0-85187201910" xml:lang="en" /> <meta name="DC.identifier" content="https://hdl.handle.net/11382/576402" scheme="DCTERMS.URI" /> <meta name="DC.language" content="eng" xml:lang="en" scheme="DCTERMS.RFC1766" /> <meta name="DC.relation" content="12" xml:lang="en" /> <meta name="DC.identifier" content="10.1088/2057-1976/ad2e35" xml:lang="en" /> <meta name="DC.relation" content="3" xml:lang="en" /> <meta name="DCTERMS.issued" content="2024" scheme="DCTERMS.W3CDTF" /> <meta name="DC.type" content="262" /> <meta name="DCTERMS.available" content="2025-02-18T17:44:43Z" scheme="DCTERMS.W3CDTF" /> <meta name="DC.title" content="Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals" xml:lang="en" /> <meta name="DC.relation" content="10" xml:lang="en" /> <meta name="DC.date" content="2025-02-18T17:44:43Z" xml:lang="*" scheme="DCTERMS.W3CDTF" /> <meta name="DC.type" content="1 Contributo su Rivista::1.1 Articolo su Rivista/Article" xml:lang="it" /> <meta name="DC.description" content="reserved" xml:lang="en" /> <meta name="DC.type" content="info:eu-repo/semantics/article" /> <meta name="DC.description" content="Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, and Kappa index of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. 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Delisle-Rodriguez, Denis; Jaramillo-Isaza, Sebasti谩n; Ruiz-Olaya, Andr茅s Felipe; Frizera-Neto, Anselmo; Ferreira de Souza, Alberto; Bastos-Filho, Teodiano" xml:lang="en" /> <meta name="DC.description" content="Blanco-D铆az, Cristian Felipe; Guerrero-Mendez, Cristian David; Delisle-Rodriguez, Denis; Jaramillo-Isaza, Sebasti谩n; Ruiz-Olaya, Andr茅s Felipe; Frizera-Neto, Anselmo; Ferreira de Souza, Alberto; Bastos-Filho, Teodiano" /> <meta name="citation_title" content="Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals" /> <meta name="citation_author" content="Blanco-D铆az, Cristian Felipe" /> <meta name="citation_author_email" content="CristianFelipe.BlancoDiaz@santannapisa.it" /> <meta name="citation_author" content="Guerrero-Mendez, Cristian David" /> <meta name="citation_author" content="Delisle-Rodriguez, Denis" /> <meta name="citation_author" content="Jaramillo-Isaza, Sebasti谩n" /> <meta 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CinecaSiteIntroduction" id="headerText"> <div> IRIS</div> </div> </div> <div class="container-xxl ps-0 pe-0 layout-no-sidebar" id="mainContainerDiv"> <main class="layout-main" id="content"> <div class="container-fluid page-content"> <nav aria-label="breadcrumb" class="mt-2 iu-hide"> <form name="breadCrumbsForm" id="breadCrumbsForm" method="post"> <ol class="breadcrumb rounded"> <li class="breadcrumb-item"><a href="/">IRIS</a></li> <li class="breadcrumb-item"><a href="/handle/11382/496815">SIARI SSSUP</a></li> <li class="breadcrumb-item"><a href="/handle/11382/496817">1 Contributo su Rivista</a></li> <li class="breadcrumb-item"><a href="/handle/11382/496776">1.1 Articolo su Rivista/Article</a></li></ol> </form> </nav> <p class="searchIndexItemDescription abstractEng">Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, and Kappa index of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.</p> <div class="row"> <div class="col-lg-12 col-xl-9"> <div class="card"> <div class="card-body"> <div> <h3 class="mb-1 text-secondary">Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals</h3> <h5> <em><span class="contributor internalContributor internalContributord4c052cb-4cf7-4876-a62f-7a41d66c6d69 m-1"><a href="/cris/rp/rp79202" class="authority author">Blanco-D铆az, Cristian Felipe</a>;</span><span class="contributor externalContributor externalContributord4c052cb-4cf7-4876-a62f-7a41d66c6d69 m-1 disabled" aria-disabled="true" style="display:none;">Guerrero-Mendez, Cristian David;</span><span class="contributor externalContributor externalContributord4c052cb-4cf7-4876-a62f-7a41d66c6d69 m-1 disabled" aria-disabled="true" style="display:none;">Delisle-Rodriguez, Denis;</span><span class="contributor externalContributor externalContributord4c052cb-4cf7-4876-a62f-7a41d66c6d69 m-1 disabled" aria-disabled="true" style="display:none;">Jaramillo-Isaza, Sebasti谩n;</span><span class="contributor externalContributor externalContributord4c052cb-4cf7-4876-a62f-7a41d66c6d69 m-1 disabled" aria-disabled="true" style="display:none;">Ruiz-Olaya, Andr茅s Felipe;</span><span class="contributor externalContributor externalContributord4c052cb-4cf7-4876-a62f-7a41d66c6d69 m-1 disabled" aria-disabled="true" style="display:none;">Frizera-Neto, Anselmo;</span><span class="contributor externalContributor externalContributord4c052cb-4cf7-4876-a62f-7a41d66c6d69 m-1 disabled" aria-disabled="true" style="display:none;">Ferreira de Souza, Alberto;</span><span class="contributor externalContributor externalContributord4c052cb-4cf7-4876-a62f-7a41d66c6d69 m-1 disabled" aria-disabled="true" style="display:none;">Bastos-Filho, Teodiano</span></em> <span onclick="JQ('.externalContributord4c052cb-4cf7-4876-a62f-7a41d66c6d69').toggle();JQ('.showExternalTextd4c052cb-4cf7-4876-a62f-7a41d66c6d69').toggle();JQ('.hideExternalTextd4c052cb-4cf7-4876-a62f-7a41d66c6d69').toggle();" class="btn btn-sm btn-outline-secondary m-1"> <span class="showExternalTextd4c052cb-4cf7-4876-a62f-7a41d66c6d69"><span class="fas fa-users" title="mostra contributor esterni"></span></span> <span class="hideExternalTextd4c052cb-4cf7-4876-a62f-7a41d66c6d69" style="display:none;"><span class="fas fa-user" title="nascondi contributor esterni"></span></span> </span> </h5> <h5>2024-01-01</h5> <hr/> <h4 class="mb-1">Abstract</h4> Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, and Kappa index of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. 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Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals