Rehabilitation Modulates High-Order Interactions Among Large-Scale Brain Networks in Subacute Stroke

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rel="alternate" hreflang="en" href="https://www.peeref.com/works/33761910" > <span>English</span> </a> <a rel="alternate" hreflang="zh" href="https://www.peeref.com/zh/works/33761910" > <span>中文</span> </a> </div> </li> </ul> </ul> </div> </nav> <main> <div id="top-info-banner" class="container-fluid mb-0"> <div class="container"> <div class="d-flex align-items-center" style="margin-top: 30px;"> <span class="text-white"> <strong class="f18">☆</strong> <span class="f16">4.7</span> </span> <span class="mx-3"></span> <span class="tag">Article</span> </div> <h1 class="title title-for-article"> Rehabilitation Modulates High-Order Interactions Among Large-Scale Brain Networks in Subacute Stroke </h1> <div class="help-links-left"> <p class="pub-info"> IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2023) </p> </div> </div> </div> <div id="article-sticky-navbar"> <div class="container"> <div class="d-flex justify-content-between flex-wrap flex-md-nowrap"> <div class="d-flex 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href="https://www.peeref.com/zh/journals/3408/ieee-transactions-on-neural-systems-and-rehabilitation-engineering"> IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING </a> </h5> <span> 卷 31, 期 -, 页码 4549-4560 </span> </div> </div> <div class="mb-3 pb-3"> <h4 class="mt-0">出版社</h4> <div class="f16"> <h5 class="title f16 text-primary"> IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC </h5> <div class="my-2"> DOI: 10.1109/TNSRE.2023.3332114 </div> </div> </div> <div class="mb-3 pb-3"> <h4 class="mt-0">关键词</h4> <div class="f16"> EEG; functional connectivity; granger causality; high-order interactions; redundancy; rehabilita-tion; resting-state networks; synergy; stroke </div> </div> <div class="mb-3 pb-3"> <h4 class="mt-0">类别</h4> <div class="f16"> <span class="d-block"> <a href="https://www.peeref.com/zh/works/list?category=Engineering%2C+Biomedical" target="_blank" class="text-dark btn btn-link p-0 text-left"> Engineering, Biomedical </a> </span> <span class="d-block"> <a href="https://www.peeref.com/zh/works/list?category=Rehabilitation" target="_blank" class="text-dark btn btn-link p-0 text-left"> Rehabilitation </a> </span> </div> </div> </div> <div class="f15 panel-box rounded shadow-none border"> <h4 class="mt-0 text-center">向作者/读者索取更多资源</h4> <div class="requests"> <div class="requests-item"> <div class="icon"> <img src="https://peeref-open.s3.amazonaws.com/images/file.png" alt=""> </div> <h4>Protocol</h4> <p> <a href="https://www.peeref.com/zh/works/33761910/resource" class="btn btn-outline-primary btn-sm"> 社区支持 </a> </p> </div> <div class="requests-item"> <div class="icon"> <img src="https://peeref-open.s3.amazonaws.com/images/experiment.png" alt=""> </div> <h4>Reagent</h4> <p> <a href="https://www.peeref.com/zh/works/33761910/resource" class="btn btn-outline-primary btn-sm"> 社区支持 </a> </p> </div> </div> </div> </div> <div class="col-md-8 px-0 pl-md-3"> <div id="article-summary-panel" class="mb-4"> <ul class="nav nav-tabs" style="list-style: none; padding-left: 0;"> <li class="active"> <a href="#ai_summary" data-toggle="tab" class="summary-tab mx-0 f16 text-dark"> <strong>智能总结</strong> <strong class="text-danger ml-1"><i>New</i></strong> </a> </li> <li class=""> <a href="#raw_abstract" data-toggle="tab" class="abstract-tab mx-0 f16 text-dark"> <strong>摘要</strong> </a> </li> </ul> <div class="tab-content border border-top-0"> <div id="ai_summary" class="tab-pane active"> <div class="summary-panel panel-box mb-0 rounded shadow-none"> <div class="f16">The study investigates interactions among brain networks in subacute stroke patients after motor rehabilitation using electroencephalography signals. The results show an increase in synergy among these networks after rehabilitation, particularly driven by the motor network. Additionally, changes in inter-network connectivity are associated with motor recovery, with the executive control network playing a relevant mediating role.</div> </div> </div> <div id="raw_abstract" class="tab-pane "> <div class="abstract-panel panel-box mb-0 rounded shadow-none"> <div class="f16">The recovery of motor functions after stroke is fostered by the functional integration of large-scale brain networks, including the motor network (MN) and high-order cognitive controls networks, such as the default mode (DMN) and executive control (ECN) networks. In this paper, electroencephalography signals are used to investigate interactions among these three resting state networks (RSNs) in subacute stroke patients after motor rehabilitation. A novel metric, the O-information rate (OIR), is used to quantify the balance between redundancy and synergy in the complex high-order interactions among RSNs, as well as its causal decomposition to identify the direction of information flow. The paper also employs conditional spectral Granger causality to assess pairwise directed functional connectivity between RSNs. After rehabilitation, a synergy increase among these RSNs is found, especially driven by MN. From the pairwise description, a reduced directed functional connectivity towards MN is enhanced after treatment. Besides, inter-network connectivity changes are associated with motor recovery, for which the mediation role of ECN seems to play a relevant role, both from pairwise and high-order interactions perspective.</div> </div> </div> </div> </div> <div class="f15 panel-box rounded shadow-none border"> <h4 class="mt-0 heading-count">作者</h4> <div class="mb-3"> <article-authors tid="33761910" list="[{"name":"I. Pirovano","sequence":1},{"name":"Y. Antonacci","sequence":2},{"name":"A. Mastropietro","sequence":3},{"name":"C. Bara","sequence":4},{"name":"L. Sparacino","sequence":5},{"name":"E. Guanziroli","sequence":6},{"name":"F. Molteni","sequence":7},{"name":"M. Tettamanti","sequence":8},{"name":"L. Faes","sequence":9},{"name":"G. Rizzo","sequence":10}]" verified="[]" page="work" ></article-authors> </div> <div class="alert alert-warning mb-0"> <h5 class="mt-0 bg-warning text-dark px-3 rounded d-inline-block"> 我是这篇论文的作者 </h5> <div class="font-weight-bold f13"> 点击您的名字以认领此论文并将其添加到您的个人资料中。 </div> </div> </div> <div class="f15 panel-box rounded shadow-none border"> <h4 class="mt-0 heading-count">评论</h4> <div class="d-flex flex-wrap flex-md-nowrap"> <div class="flex-grow-1"> <h4 class="f16"> 主要评分 <a href="javascript:;" data-toggle="tooltip" data-placement="right" title="主要评分表示论文的整体质量水平。"> <i class="ivu-icon ivu-icon-md-help-circle f18 ml-2"></i> </a> </h4> <div class="d-flex flex-wrap flex-md-nowrap align-items-center alert mb-0"> <div class="d-flex align-items-center justify-content-center"> <Rate disabled allow-half value="4.7" style="font-size: 28px;"></Rate> <strong class="f20 m-3" style="color: #f5a623;">4.7</strong> </div> <div class="text-muted mx-4"> 评分不足 </div> </div> <h4 class="f16"> 次要评分 <a href="javascript:;" data-toggle="tooltip" data-placement="right" title="次要评分独立反映论文的优点或缺点。"> <i class="ivu-icon ivu-icon-md-help-circle f18 ml-2"></i> </a> </h4> <div class="d-flex flex-wrap flex-md-nowrap alert"> <div class="d-flex flex-shrink-0 align-items-center mr-3"> <h5 class="my-0">新颖性</h5> <strong class="mx-4">-</strong> </div> <div class="d-flex flex-shrink-0 align-items-center mr-3"> <h5 class="my-0">重要性</h5> <strong class="mx-4">-</strong> </div> <div class="d-flex flex-shrink-0 align-items-center mr-3"> <h5 class="my-0">科学严谨性</h5> <strong class="mx-4">-</strong> </div> </div> </div> <div class="flex-shrink-0"> <div class="border bg-light py-2 px-4"> <h5 class="mb-1">评价这篇论文</h5> <Rate class="f24" @on-change="function(value){ location.href='https://www.peeref.com/zh/works/33761910/comments?rating='+value }"></Rate> </div> </div> </div> </div> <div id="collection" class="f15 panel-box rounded shadow-none border"> <h4 class="mt-0 heading-count">推荐</h4> <div class="my-3"> <ul class="nav nav-pills border-bottom pb-3" style="list-style: none; padding-left: 0;"> <li class="active"> <a href="#articles_from_related" data-toggle="tab" class="mx-0 f15"> <strong>相关</strong> </a> </li> <li class=""> <a href="#articles_from_authors" data-toggle="tab" class="mx-0 f15"> <strong>来自同一作者</strong> </a> </li> <li class=""> <a href="#articles_from_journal" data-toggle="tab" class="mx-0 f15"> <strong>来自同一期刊</strong> </a> </li> </ul> <div class="tab-content"> <div id="articles_from_related" class="tab-pane active"> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Computer Science, Interdisciplinary Applications </span> </div> <h4> <a href="https://www.peeref.com/zh/works/24763682" class="text-dark hover-underline">Hybrid machine learning method for a connectivity-based epilepsy diagnosis with resting-state EEG</a> </h4> <p class="text-ellipsis-2">Berjo Rijnders, Emin Erkan Korkmaz, Funda Yildirim</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/5779.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study investigates the performance of a convolutional neural network (CNN) algorithm on epilepsy diagnosis. The approach achieves a high diagnostic accuracy by using deep learning to learn connectivity patterns directly from easily acquired EEG data. This method could prove valuable as a clinical decision support system for epilepsy diagnosis. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING</span> (2022) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/24763682/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Neurosciences </span> </div> <h4> <a href="https://www.peeref.com/zh/works/26179679" class="text-dark hover-underline">Correction of global physiology in resting-state functional near-infrared spectroscopy</a> </h4> <p class="text-ellipsis-2">Pradyumna Lanka, Heather Bortfeld, Theodore J. Huppert</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/10234.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> In resting-state fNIRS data, a processing pipeline incorporating pre-whitening, robust statistical methods, and partial correlation can effectively reduce autocorrelation, motion artifacts, and global physiology, obtaining statistically valid connectivity metrics. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">NEUROPHOTONICS</span> (2022) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/26179679/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Multidisciplinary </span> </div> <h4> <a href="https://www.peeref.com/zh/works/27100397" class="text-dark hover-underline">EEG-Based Mapping of Resting-State Functional Brain Networks in Patients with Parkinson's Disease</a> </h4> <p class="text-ellipsis-2">Sarah Leviashvili, Yael Ezra, Amgad Droby, Hao Ding, Sergiu Groppa, Anat Mirelman, Muthuraman Muthuraman, Inbal Maidan</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> The study found reduced connectivity in the Central Executive Network (CEN) and Dorsal Attention Network (DAN) in Parkinson's disease patients, while increased connectivity was observed in the Ventral Attention Network (VAN). These results indicate a complex pattern of DFC alteration within major brain networks, reflecting the co-occurrence of impairment and compensatory mechanisms processes taking place in PD. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">BIOMIMETICS</span> (2022) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/27100397/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Clinical Neurology </span> </div> <h4> <a href="https://www.peeref.com/zh/works/81587658" class="text-dark hover-underline">Abnormal Functional Connectivity Intra- and Inter-Network in Resting-State Brain Networks of Patients with Toothache</a> </h4> <p class="text-ellipsis-2">Yuping Zhu, Xunfu Lai, Mengting Wang, Xin Tang, Tianyi Wan, Bin Li, Xiaoming Liu, Jialin Wu, Lei He, Yulin He</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/10061.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study used independent component analysis to separate the resting-state functional network components of patients with dental pain and healthy controls, and found functional connectivity abnormalities in cognitive-emotion-related brain networks in patients with dental pain. These abnormal changes may be related to the clinical symptoms of patients with dental pain, providing a new imaging basis for understanding the central neural mechanisms of patients with dental pain. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">JOURNAL OF PAIN RESEARCH</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/81587658/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Neurosciences </span> </div> <h4> <a href="https://www.peeref.com/zh/works/82252348" class="text-dark hover-underline">Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants</a> </h4> <p class="text-ellipsis-2">Rikkert Hindriks, Tommy A. A. Broeders, Menno M. Schoonheim, Linda Douw, Fernando Santos, Wessel van Wieringen, Prejaas K. B. Tewarie</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/3266.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> We introduce and evaluate the performance of higher-order functional connectivity metrics using multivariate cumulants for resting-state fMRI data. These metrics can quantify connectivity that cannot be explained by pairwise Pearson correlations. Using a generative model, we assess the bias, standard errors, and detection probabilities in individual and group-level data. Application to resting-state fMRI data and a clinical cohort of patients with multiple sclerosis demonstrates that higher-order connectivity can capture relevant non-redundant information. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">HUMAN BRAIN MAPPING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/82252348/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Neurosciences </span> </div> <h4> <a href="https://www.peeref.com/zh/works/25827063" class="text-dark hover-underline">Specific subsystems of the inferior parietal lobule are associated with hand dysfunction following stroke: A cross-sectional resting-state fMRI study</a> </h4> <p class="text-ellipsis-2">FeiWen Liu, ChangCheng Chen, ZhongFei Bai, WenJun Hong, SiZhong Wang, ChaoZheng Tang</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/1873.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study aimed to explore the relationship between the disrupted IPL subsystems and hand dysfunction following chronic stroke. By comparing patients with stroke and healthy controls, it was found that the IPL subsystems in stroke patients exhibited abnormalities and were associated with hand performance. Specific reorganizations of IPL subsystems were associated with hand dysfunction following chronic stroke. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">CNS NEUROSCIENCE & THERAPEUTICS</span> (2022) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/25827063/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Neurosciences </span> </div> <h4> <a href="https://www.peeref.com/zh/works/84019306" class="text-dark hover-underline">Association of Exercise with Better Olfactory Performance and Higher Functional Connectivity Between the Olfactory Cortex and the Prefrontal Cortex: A Resting-State Functional Near-Infrared Spectroscopy Study</a> </h4> <p class="text-ellipsis-2">Chenping Zhang, Xiaochun Wang</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/10889.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> The study compared the functional connectivity between the olfactory cortex and the PFC in healthy individuals who exercised regularly and those who did not. The results showed that the exercise group had a significantly lower threshold for detecting odors and stronger connectivity between the olfactory cortex and the PFC. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">BRAIN CONNECTIVITY</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/84019306/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Electrical & Electronic </span> </div> <h4> <a href="https://www.peeref.com/zh/works/34472427" class="text-dark hover-underline">A New Framework for the Time- and Frequency-Domain Assessment of High-Order Interactions in Networks of Random Processes</a> </h4> <p class="text-ellipsis-2">Luca Faes, Gorana Mijatovic, Yuri Antonacci, Riccardo Pernice, Chiara Bara, Laura Sparacino, Marco Sammartino, Alberto Porta, Daniele Marinazzo, Sebastiano Stramaglia</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/3419.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study introduces a new approach to evaluate pairwise and higher-order interactions in multivariate rhythmic processes. By introducing the O-information rate metric and utilizing the spectral representation, this approach allows for a better understanding of redundant and synergistic interactions in dynamic networks. The validation of the framework on physiological and brain networks demonstrates its capability to identify relevant informational circuits and physiological mechanisms. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON SIGNAL PROCESSING</span> (2022) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/34472427/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Computer Science, Artificial Intelligence </span> </div> <h4> <a href="https://www.peeref.com/zh/works/26563050" class="text-dark hover-underline">EEG Interchannel Causality to Identify Source/Sink Phase Connectivity Patterns in Developmental Dyslexia</a> </h4> <p class="text-ellipsis-2">I. Rodriguez-Rodriguez, A. Ortiz, N. J. Gallego-Molina, M. A. Formoso, W. L. Woo</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/3818.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> While the cause-effect relationships of the brain connectivity network in developmental dyslexia have not been thoroughly examined, this study proposes a method to calculate directional connectivity using electroencephalography signals and white noise stimuli. The findings confirm the right-lateralized Theta sampling network anomaly and show that it is more pronounced in the causal relationships of channels acting as sinks. The proposed method can be used for both classification and exploratory analysis, with high accuracy and AUC values achieved. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">INTERNATIONAL JOURNAL OF NEURAL SYSTEMS</span> (2023) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/26563050/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Neurosciences </span> </div> <h4> <a href="https://www.peeref.com/zh/works/26153888" class="text-dark hover-underline">Brain and brain-heart Granger causality during wakefulness and sleep</a> </h4> <p class="text-ellipsis-2">Helmi Abdalbari, Mohammad Durrani, Shivam Pancholi, Nikhil Patel, Slawomir J. Nasuto, Nicoletta Nicolaou</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/9767.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> In this exploratory study, Granger Causality (GC) was used to investigate the interactions between the brain and heart during wakefulness and sleep. The results revealed significant differences between wakefulness and different sleep stages, and confirmed the existence of bidirectional connections between the brain and heart. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">FRONTIERS IN NEUROSCIENCE</span> (2022) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/26153888/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Computer Science, Information Systems </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83939434" class="text-dark hover-underline">Effective Connectivity Estimation by a Hybrid Neural Network, Empirical Wavelet Transform, and Bayesian Optimization</a> </h4> <p class="text-ellipsis-2">Milad Esmaeil-Zadeh, Morteza Fattahi, Mohammad Soltani-Gol, Reza Rostami, Hamid Soltanian-Zadeh</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/9464.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study proposes an effective connectivity model based on a hybrid neural network model using Empirical Wavelet Transform and Long Short-Term Memory Network, and selects the best hyperparameters and time lags through Bayesian Optimization. The model outperforms other neural networks on simulated data and is robust against noise. The results using real EEG data are consistent with previous studies. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83939434/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Neurosciences </span> </div> <h4> <a href="https://www.peeref.com/zh/works/25441214" class="text-dark hover-underline">Power shift and connectivity changes in healthy aging during resting-state EEG</a> </h4> <p class="text-ellipsis-2">Alessio Perinelli, Sara Assecondi, Chiara F. Tagliabue, Veronica Mazza</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/6150.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study found that there is a shift from posterior to anterior areas in the neural activity of older adults. The connectivity between frontal, parietal, and temporal areas is strengthened, while the intra-area connections in the frontal areas are reduced. Additionally, the network modularity decreases with age. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">NEUROIMAGE</span> (2022) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/25441214/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Biochemical Research Methods </span> </div> <h4> <a href="https://www.peeref.com/zh/works/23527497" class="text-dark hover-underline">Resting State Functional Connectivity Analysis During General Anesthesia: A High-Density EEG Study</a> </h4> <p class="text-ellipsis-2">Hui Bi, Shumei Cao, Hanying Yan, Zhongyi Jiang, Jun Zhang, Ling Zou</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/3431.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> Monitoring the depth of anesthesia is important for administering general anesthetics during surgery. However, traditional EEG monitors have limitations in monitoring conscious states. This study used high-density EEG signals to compare two methods for functional connectivity analysis before and after anesthesia-induced loss of consciousness. The results show that the method based on sparse representation performs better in distinguishing loss of consciousness from awake states. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS</span> (2022) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/23527497/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Neurosciences </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83318431" class="text-dark hover-underline">EEG alpha band functional brain network correlates of cognitive performance in children after perinatal stroke</a> </h4> <p class="text-ellipsis-2">Alja Kavcic, Dasa Kocjancic Borko, Jana Kodric, Dejan Georgiev, Jure Demsar, Aneta Soltirovska-Salamon</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/6150.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> The study aimed to explore the correlation between functional brain network properties and cognitive functions in children after perinatal stroke. By analyzing resting-state functional connectomes and cognitive evaluations, it was found that the functional brain networks after perinatal stroke had lower modularity, higher clustering coefficient, and other characteristics, and these network features were correlated with cognitive functions. The results suggest that specific cognitive functions are associated with different brain network properties, and the functional network characteristics after perinatal stroke reflect poorer cognitive functioning. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">NEUROIMAGE</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83318431/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 "> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Neurosciences </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83002750" class="text-dark hover-underline">The subcortical brain regions influence the cortical areas during resting-state: an fMRI study</a> </h4> <p class="text-ellipsis-2">Omid Moazeni, Georg Northoff, Seyed Amir Hossein Batouli</p> <div class="d-flex mb-3"> <div class="flex-shrink-0 d-none d-sm-block"> <img src="https://peeref-open.s3.amazonaws.com/storage/images/covers/2942.jpg" alt="" class="border mr-3" width="100"> </div> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study investigated neural activity patterns in the brain during the resting state and found that these patterns are constantly changing. The researchers hypothesized that brain activation itself is responsible for this change and analyzed fMRI data. The results showed that subcortical regions have a greater influence on cortical regions, which helps to better understand the dynamic nature of brain functions. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">FRONTIERS IN HUMAN NEUROSCIENCE</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83002750/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> </div> <div id="articles_from_authors" class="tab-pane "> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Review </span> <span class="d-inline-block badge badge-cyan"> Psychology, Clinical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83739724" class="text-dark hover-underline">The Neurofunctional Correlates of Morphosyntactic and Thematic Impairments in Aphasia: A Systematic Review and Meta-analysis</a> </h4> <p class="text-ellipsis-2">Sabrina Beber, Giorgia Bontempi, Gabriele Miceli, Marco Tettamanti</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> Lesion-symptom studies in aphasia patients showed that left temporoparietal damage is associated with impaired ability to process thematic roles in the comprehension of semantically reversible sentences, challenging the traditional view that left prefrontal regions are critical for sentence comprehension. The study also found that prefrontal regions are involved in sentence processing, and that morphosyntactic processing correlates more with prefrontal structures than with temporoparietal regions, while thematic role assignment shows the opposite trend. Finally, the study discusses current limitations in the literature and proposes a set of recommendations to clarify unresolved issues. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">NEUROPSYCHOLOGY REVIEW</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83739724/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Chemistry, Analytical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/82000015" class="text-dark hover-underline">Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements</a> </h4> <p class="text-ellipsis-2">Gabriele Volpes, Simone Valenti, Giuseppe Genova, Chiara Bara, Antonino Parisi, Luca Faes, Alessandro Busacca, Riccardo Pernice</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> Wearable health devices are developing rapidly. This study introduces a new type of wearable biomedical device that can simultaneously acquire multiple biosignals and extract important physiological indicators to assess physiological states and detect potential stress conditions. Preliminary measurement results show that the device can detect changes between rest and stress states, and simultaneous acquisition of PPG and ECG signals can compare HRV and PRV indicators. The study also confirms the limitations of wearable devices during physical activity. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">BIOSENSORS-BASEL</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/82000015/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Biophysics </span> </div> <h4> <a href="https://www.peeref.com/zh/works/81742520" class="text-dark hover-underline">Positional contrastive learning for improved thigh muscle segmentation in MR images</a> </h4> <p class="text-ellipsis-2">Nicola Casali, Elisa Scalco, Maria Giovanna Taccogna, Fulvio Lauretani, Simone Porcelli, Andrea Ciuni, Alfonso Mastropietro, Giovanna Rizzo</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> We propose a positional contrastive self-supervised learning method for thigh muscle segmentation and evaluate it on limited annotated data. The results show that the method can significantly improve the segmentation performance when using a very limited number of labeled volumes, and can also achieve enhancements when using all labeled objects. This has potential implications for accelerating the annotation process in the clinic. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">NMR IN BIOMEDICINE</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/81742520/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83092041" class="text-dark hover-underline">A Method to Assess Granger Causality, Isolation and Autonomy in the Time and Frequency Domains: Theory and Application to Cerebrovascular Variability</a> </h4> <p class="text-ellipsis-2">Laura Sparacino, Yuri Antonacci, Chiara Bara, Angela Valenti, Alberto Porta, Luca Faes</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study proposes the concepts of Granger Isolation (GI) and Granger Autonomy (GA) for assessing the dynamics of coupled physiologic processes. By embedding into a linear parametric framework, GC from driver X to target process Y is computed, along with GI and a new spectral measure of GA. Simulations and applications show that these measures complement GC and detect pathophysiological responses to postural stress. This study provides new methods and perspectives for analyzing coupled processes. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83092041/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Biotechnology & Applied Microbiology </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83164340" class="text-dark hover-underline">Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model</a> </h4> <p class="text-ellipsis-2">Alfonso Mastropietro, Nicola Casali, Maria Giovanna Taccogna, Maria Grazia D'Angelo, Giovanna Rizzo, Denis Peruzzo</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study investigated the performance of SwinT and CNN in classifying neuromuscular disorders using skeletal muscle MRI scans. The results showed that SwinT had higher accuracy, especially when using fat fraction images as input. The study also suggested that AI-driven approaches have potential applications in precise classification of neuromuscular disorders and improving patient care. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">BIOENGINEERING-BASEL</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83164340/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Physics, Multidisciplinary </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83784162" class="text-dark hover-underline">Disentangling high-order effects in the transfer entropy</a> </h4> <p class="text-ellipsis-2">Sebastiano Stramaglia, Luca Faes, Jesus M. Cortes, Daniele Marinazzo</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This paper proposes a novel method to calculate transfer entropy (TE), which decomposes TE into unique, redundant, and synergistic atoms, enabling the quantification of the relative importance of high-order effects in information transfer and highlighting the processes that contribute to building these effects along with the driver. The application of this method in climatology is demonstrated. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">PHYSICAL REVIEW RESEARCH</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83784162/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Microbiology </span> </div> <h4> <a href="https://www.peeref.com/zh/works/82614142" class="text-dark hover-underline">Gut Microbiota Ecological and Functional Modulation in Post-Stroke Recovery Patients: An Italian Study</a> </h4> <p class="text-ellipsis-2">Riccardo Marsiglia, Chiara Marangelo, Pamela Vernocchi, Matteo Scanu, Stefania Pane, Alessandra Russo, Eleonora Guanziroli, Federica Del Chierico, Massimiliano Valeriani, Franco Molteni, Lorenza Putignani</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study investigated the relationship between ischemic stroke (IS) and the gut microbiota (GM). The researchers found that fecal metabolite concentrations, such as short-chain fatty acids (SCFAs), were higher in the GM of IS patients, while indole and 3-methylindole (skatole) were decreased. In addition, the expanded population of Akkermansia and the enrichment of acetic acid could be considered potential disease phenotype signatures. These findings suggest that IS may affect the gut microbial environment and provide new insights for the diagnosis and treatment of IS. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">MICROORGANISMS</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/82614142/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Mathematics, Interdisciplinary Applications </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83839940" class="text-dark hover-underline">Chaotic dynamics and synchronization under tripartite couplings: Analyses and experiments using single-transistor oscillators as metaphors of neural dynamics</a> </h4> <p class="text-ellipsis-2">Ludovico Minati, Laura Sparacino, Luca Faes, Hiroyuki Ito, Chunbiao Li, Pedro A. Valdes-Sosa, Mattia Frasca, Stefano Boccaletti</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study uses a basic electronic model to investigate the synchronization and dynamics of systems where traditional pairwise interaction models are not applicable, such as tripartite synapses and thalamocortical modulation in the human brain. The model consists of three single-transistor chaotic oscillators with tripartite couplings, and all pairwise interactions are also modulated by a third node. Detailed circuit simulations and experiments revealed that high-order interactions profoundly shape the dynamics, promoting the onset of chaos and complex interdependence. Recordings by scanning the intensities of the bipartite and tripartite couplings showed that the influence of the coupling scheme may lead to partially generalizable effects. Further insights into directed interdependencies were obtained by applying information-theoretical approaches. Additional simulations of a triplet of parametrically identical Rössler systems confirmed the universality of the experimental results and emphasized that tripartite couplings can give rise to complex behaviors, including multistability, that do not occur with only bipartite couplings. A simplified stability analysis was also performed. These results motivate future experimental work focused on tripartite couplings in other connection topologies and complex networks. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">CHAOS SOLITONS & FRACTALS</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83839940/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83031815" class="text-dark hover-underline">Experimental Validation of an Upper Limb Benchmarking Framework in Healthy and Post-Stroke Individuals: A Pilot Study</a> </h4> <p class="text-ellipsis-2">Valeria Longatelli, Clara B. Sanz-Morere, Diego Torricelli, Paula Martos Hernandez, Eleonora Guanziroli, Jesus Tornero, Franco Molteni, Jose L. Pons, Alessandra Pedrocchi, Marta Gandolla</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This paper presents the experimental validation of a benchmarking scheme for upper limb functional evaluation. The scheme is repeatable among different assessors and instrumentation, and not affected by age. In post-stroke patients, the scheme can detect the decline in motor performance. The scheme has wide applicability and can be used as a valuable tool in the clinical routine. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83031815/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Biophysics </span> </div> <h4> <a href="https://www.peeref.com/zh/works/84104398" class="text-dark hover-underline">Comparison of automatic and physiologically-based feature selection methods for classifying physiological stress using heart rate and pulse rate variability indices</a> </h4> <p class="text-ellipsis-2">Marta Iovino, Ivan Lazic, Tatjana Loncar-Turukalo, Michal Javorka, Riccardo Pernice, Luca Faes</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study evaluated the classification performance of four machine learning algorithms for physiological stress, compared two feature selection methods, and found that the automatic feature selection method was better, and PRV features performed comparably to HRV features, which is helpful for outpatient monitoring. The results of the study are helpful in identifying relevant features for stress classification and have important implications for advancing stress assessment methods. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">PHYSIOLOGICAL MEASUREMENT</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/84104398/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/84257943" class="text-dark hover-underline">Assessing High-Order Links in Cardiovascular and Respiratory Networks via Static and Dynamic Information Measures</a> </h4> <p class="text-ellipsis-2">Gorana Mijatovic, Laura Sparacino, Yuri Antonacci, Michal Javorka, Daniele Marinazzo, Sebastiano Stramaglia, Luca Faes</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study introduces a framework that combines the assessment of high-order interactions with statistical inference to characterize the functional links sustaining physiological networks. The framework can be applied to both static and dynamic networks and is able to detect statistical structures associated with cascade, common drive, and common target effects. Applying it to the cardiovascular network allows for non-invasive characterization of cardiovascular control mechanisms. This method provides a new way to assess physiological interactions and complements existing strategies for classifying pathophysiological states. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/84257943/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Physics, Multidisciplinary </span> </div> <h4> <a href="https://www.peeref.com/zh/works/33715026" class="text-dark hover-underline">Gradients of O-information: Low-order descriptors of high-order dependencies</a> </h4> <p class="text-ellipsis-2">T. Scagliarini, D. Nuzzi, Y. Antonacci, L. Faes, F. E. Rosas, D. Marinazzo, S. Stramaglia</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> O-information is a metric that measures the balance between redundant and synergistic information in groups of three or more variables. This study proposes using the gradients of O-information as low-order descriptors to characterize localized high-order effects in a system. The authors demonstrate the framework's capabilities through various examples, including Ising models with frustration and three-spin interactions, as well as a linear vector autoregressive process. They also provide a practical data analysis example using U.S. macroeconomic data. The theoretical and empirical analyses highlight the potential of these gradients in identifying the contribution of variables in high-order informational circuits. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">PHYSICAL REVIEW RESEARCH</span> (2023) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/33715026/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 "> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/33408552" class="text-dark hover-underline">Role of the EEG Theta Network During Software Production: A Connectivity Study</a> </h4> <p class="text-ellipsis-2">A. Calcagno, S. Coelli, C. Amendola, I. Pirovano, R. Re, J. Medeiros, P. Carvalho, H. Madeira, A. M. Bianchi</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> In this study, EEG signals were analyzed from 11 experienced programmers to investigate brain activities during realistic programming and reading tasks. The results showed that both tasks were supported by modulations of the Theta fronto-parietal network, with parietal areas acting as sources of information and frontal areas acting as receivers. Realistic programming led to increased Theta power and changes in network topology, especially in the parietal area, suggesting task-related adaptation of the supporting network system. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2023) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/33408552/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> </div> <div id="articles_from_journal" class="tab-pane "> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/82954913" class="text-dark hover-underline">Physiologic Network-Based Brain-Heart Interaction Quantification During Visual Emotional Elicitation</a> </h4> <p class="text-ellipsis-2">Zhipeng Cai, Hongxiang Gao, Min Wu, Jianqing Li, Chengyu Liu</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study investigated the interaction between the brain and the heart during emotional processing using a network physiology approach. It found that low-frequency components in the EEG play a crucial role in information transmission, and delta-theta coupling in the frontal pole regions regulates emotional responses. Significant changes in the theta frequency band were also observed across different emotional states. These findings provide novel insights into neurophysiology and emotion research. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/82954913/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Review </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/84357019" class="text-dark hover-underline">A Systematic Review on Rigid Exoskeleton Robot Design for Wearing Comfort: Joint Self-Alignment, Attachment Interface, and Structure Customization</a> </h4> <p class="text-ellipsis-2">Longbao Chen, Ding Zhou, Yuquan Leng</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This paper studies the factors affecting the comfort of exoskeleton robots, including design and structural configuration, and evaluates the pros and cons of related design methods. It also proposes the direction and solutions for future research to improve the comfort of exoskeleton robots. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/84357019/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/84286880" class="text-dark hover-underline">Brain Activation Pattern Caused by Soft Rehabilitation Glove and Virtual Reality Scenes: A Pilot fNIRS Study</a> </h4> <p class="text-ellipsis-2">Pengju Liu, Xinyi Yang, Fenglin Han, Guangshuai Peng, Qiao Li, Liping Huang, Lizhen Wang, Yubo Fan</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study investigated the effects of a soft rehabilitation glove and virtual reality scenes on brain activation patterns. The results showed that VR and VRA tasks induced greater cortical activation, and stroke patients had higher cortical activation but weaker functional connectivity. The study also found that multi-sensory stimulation can promote functional communication between brain regions in patients, which has potential for neuromodulation in rehabilitation training. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/84286880/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83032796" class="text-dark hover-underline">Neurophysiologically Meaningful Motor Imagery EEG Simulation With Applications to Data Augmentation</a> </h4> <p class="text-ellipsis-2">Catalina M. Galvan, Ruben D. Spies, Diego H. Milone, Victoria Peterson</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> PySimMIBCI is a framework for generating realistic MI-EEG signals by integrating neurophysiologically meaningful activities. It can simulate different user capabilities to control an MI-BCI and fatigue effects. The simulated data is similar to the real data, and the data augmentation strategy based on the simulated data outperforms other methods, improving the performance of DL models. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83032796/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/82955358" class="text-dark hover-underline">Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG</a> </h4> <p class="text-ellipsis-2">Meng Jiao, Shihao Yang, Xiaochen Xian, Neel Fotedar, Feng Liu</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This paper presents a multi-modal deep fusion framework using attention neural networks for electrophysiological source imaging. The framework can fully leverage the complementary information between EEG and MEG to improve source localization accuracy and shows good stability in reconstructing sources with extended activation areas and low signal-to-noise ratio EEG/MEG measurements. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/82955358/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/84064454" class="text-dark hover-underline">Recurrent Neural Network Enabled Continuous Motion Estimation of Lower Limb Joints From Incomplete sEMG Signals</a> </h4> <p class="text-ellipsis-2">Gang Wang, Long Jin, Jiliang Zhang, Xiaoqin Duan, Jiang Yi, Mingming Zhang, Zhongbo Sun</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study proposes a multi-task parallel learning framework for handling incomplete sEMG signals to achieve continuous motion estimation. The framework combines a residual network and a recurrent neural network, utilizes the attention mechanism to redistribute the weight distribution, and designs a jointly optimized loss function. Experimental results show that the model performs well in handling signal missing and multi-joint continuous motion estimation, and can maintain high estimation accuracy even in the presence of multi-channel electrode sheet shedding. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/84064454/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83103986" class="text-dark hover-underline">A Dual-Adversarial Model for Cross-Time and Cross-Subject Cognitive Workload Decoding</a> </h4> <p class="text-ellipsis-2">Yang Shao, Yueying Zhou, Peiliang Gong, Qianru Sun, Daoqiang Zhang</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This paper proposes a Bi-Classifier Joint Domain Adaptation model for EEG-based cross-time and cross-subject Cognitive Workload Decoding, addressing the limitations of current solutions. The model learns common domain features and retains category differences through adversarial processes, improving the accuracy of CWD. Experimental results show that the model performs well in recognizing different cognitive workload levels. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83103986/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83051212" class="text-dark hover-underline">A Review of Motor Brain-Computer Interfaces Using Intracranial Electroencephalography Based on Surface Electrodes and Depth Electrodes</a> </h4> <p class="text-ellipsis-2">Xiaolong Wu, Benjamin Metcalfe, Shenghong He, Huiling Tan, Dingguo Zhang</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This review focuses on BCIs research using surface and depth electrodes for movement decoding on human subjects. The findings demonstrate a distributed motor-related network spanning multiple brain regions, and that surface electrodes can provide richer information. Deep learning shows superior performance in using raw signals compared to traditional machine learning algorithms. Although open-loop BCIs have achieved promising results, closed-loop BCIs with sensory feedback are still in the early stage, and the long-term implantation of both ECoG surface and depth electrodes has not been fully evaluated. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83051212/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83467041" class="text-dark hover-underline">Active Neural Network Control for a Wearable Upper Limb Rehabilitation Exoskeleton Robot Driven by Pneumatic Artificial Muscles</a> </h4> <p class="text-ellipsis-2">Haoqi Zhang, Jiade Fan, Yanding Qin, Mengqiang Tian, Jianda Han</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This paper develops a PAM-driven wearable exoskeleton robot for upper limb rehabilitation and proposes an active neural network method to address the hysteresis issue of PAM. The method does not require inversion and can update the weights dynamically. Experiments verify its effectiveness and robustness. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83467041/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/84301599" class="text-dark hover-underline">A Novel Multi-Feature Fusion Network With Spatial Partitioning Strategy and Cross-Attention for Armband-Based Gesture Recognition</a> </h4> <p class="text-ellipsis-2">Fo Hu, Mengyuan Qian, Kailun He, Wen-An Zhang, Xusheng Yang</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study proposes a novel multi-feature fusion network to improve the accuracy and robustness of gesture recognition. The network combines a spatial partitioning strategy and a cross-attention mechanism to effectively integrate the time-space-frequency information of multi-modal signals from the armband sensor. Experiments show that this method performs well on multiple datasets. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/84301599/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83538937" class="text-dark hover-underline">Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals</a> </h4> <p class="text-ellipsis-2">Fenqi Rong, Banghua Yang, Cuntai Guan</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study developed a novel MI-BCI experimental paradigm for unilateral limb multitasks, including four imagined movement directions. 46 healthy subjects participated, and various machine learning techniques were used for evaluation. To improve decoding accuracy, the MVCA method was proposed. The study shows that multi-directional motor imagery in unilateral limbs can be decoded, especially in the two directions of top right to bottom left and top left to bottom right, with the best accuracy. This study advances the development of the MI-BCI paradigm and provides preliminary evidence for decoding multi-directional information from EEG. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83538937/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83627746" class="text-dark hover-underline">An Adaptive Hybrid Brain-Computer Interface for Hand Function Rehabilitation of Stroke Patients</a> </h4> <p class="text-ellipsis-2">Jianqiang Su, Jiaxing Wang, Weiqun Wang, Yihan Wang, Chayut Bunterngchit, Pu Zhang, Zeng-Guang Hou</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study presents an adaptive hybrid BCI system combining MI and SSVEP to improve the effectiveness of hand movement recovery in stroke patients. By evoking SSVEP through visual stimulation, the recognition accuracy is enhanced, and the effective execution of MI tasks is ensured by real-time monitoring of ERD. The experimental results show that the system has achieved good results in both healthy subjects and stroke patients, demonstrating its clinical practicality. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83627746/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83496242" class="text-dark hover-underline">Integrating Large Language Model, EEG, and Eye-Tracking for Word-Level Neural State Classification in Reading Comprehension</a> </h4> <p class="text-ellipsis-2">Yuhong Zhang, Qin Li, Sujal Nahata, Tasnia Jamal, Shih-Kuen Cheng, Gert Cauwenberghs, Tzyy-Ping Jung</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study investigates the use of large language models, eye gaze, and EEG data to study how the brain processes words with different degrees of relevance during reading. By analyzing these data, the researchers found that words highly relevant to the keyword received more eye fixations and achieved a high accuracy in the word-level classification task. This study provides valuable information for understanding human cognition and developing reading assistance technologies. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83496242/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 border-bottom"> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/83104125" class="text-dark hover-underline">A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection</a> </h4> <p class="text-ellipsis-2">Hongtao Wang, Zehui Wang, Yu Sun, Zhen Yuan, Tao Xu, Junhua Li</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study proposes a cascade network structure combining xDAWN and EEGNet for detecting variable P300 signals to facilitate BCI applications and deepen the understanding of the P300 generation mechanism. The method shows better performance in both P300 speller and RSVP paradigms, capable of recognizing more symbols with fewer repetitions, higher information transfer rate, and unweighted average recall rate. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/83104125/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> <div class="my-4 "> <div> <span class="d-inline-block badge badge-blue"> Article </span> <span class="d-inline-block badge badge-cyan"> Engineering, Biomedical </span> </div> <h4> <a href="https://www.peeref.com/zh/works/82954766" class="text-dark hover-underline">Evaluation of Acupuncture Efficacy in Modulating Brain Activity With Periodic-Aperiodic EEG Measurements</a> </h4> <p class="text-ellipsis-2">Haitao Yu, Fan Li, Jialin Liu, Dongliang Liu, Haolong Guo, Jiang Wang, Guiping Li</p> <div class="d-flex mb-3"> <div class="p-3 rounded bg-light-blue"> <strong>Summary:</strong> This study designs an EEG-based monitoring system to evaluate the therapeutic effect of acupuncture on the human brain by extracting periodic-aperiodic features. The results show that acupuncture can significantly enhance brain activity, especially in the parietal and occipital lobe regions, and also reduce the aperiodic exponent, which is more significant in the central and frontal lobe regions. In addition, the monitoring system can assess the sensitivity of different brain regions to acupuncture and establish a knowledge graph. This study provides a new method for quantitatively evaluating the impact of acupuncture on the human brain. </div> </div> <div class="d-flex justify-content-between"> <p class="font-weight-bold"> <span class="text-primary">IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING</span> (2024) </p> <div class="flex-shrink-0"> <a class="btn btn-outline-primary btn-sm" href="https://www.peeref.com/zh/works/82954766/add-to-collection" target="_blank"> <strong>添加到收藏夹</strong> </a> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div class="modal fade" id="export-citation" tabindex="-1"> <div class="modal-dialog"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal"><span>×</span></button> <h4 class="modal-title">导出引文 <b class="text-primary"></b></h4> </div> <div class="modal-body"> <div class="my-3 px-4 f16"> <form action="https://www.peeref.com/zh/works/citation/download" method="GET" target="_blank"> <div 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Rehabilitation Modulates High-Order Interactions Among Large-Scale Brain Networks in Subacute Stroke - Peeref