{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T09:40:18Z","timestamp":1787910018263,"version":"build-2784847793"},"reference-count":50,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007961","name":"Melbourne School of Psychological Sciences","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007961","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Australian Government Research Training Program Scholarship"},{"name":"ARC (Australian Research Council) Industrial Transformation Training Centre in Cognitive Computing for Medical Technologies","award":["IC170100030"],"award-info":[{"award-number":["IC170100030"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2025,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Multiple sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A brain\u2013computer interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair. However, the limited BCI research in people with MS has been confined to exploring the P300 response and brain signals associated with attempted movement. The current study aims to expand the MS-BCI literature by highlighting the feasibility of decoding MS imagined movement. <jats:italic>Approach.<\/jats:italic> We collected electroencephalography data from eight participants with various symptoms of MS and ten neurotypical control participants. Participants made imagined movements of the hands and feet as directed by a go no-go protocol. Binary regularised linear discriminant analysis was used to classify imagined movement vs. rest and vs. movement at individual time-frequency points. The frequency bands which provided the maximal accuracy, and the associated latency, were compared. <jats:italic>Main Results.<\/jats:italic> In all MS participants, the classification algorithm achieved above 70% accuracy in at least one imagined movement vs. rest classification and most movement vs. movement classifications. There was no significant difference between classification of limbs with weakness or paralysis to neurotypical controls. Both the MS and control groups possessed decodable information within the alpha (7\u201313 Hz) and beta (16\u201330 Hz) bands at similar latency. <jats:italic>Significance.<\/jats:italic> This study is the first to demonstrate the feasibility of decoding imagined movements in people with MS. As an alternative to the P300 response, motor imagery-based control of a BCI may also be combined with existing motor imagery therapy to supplement MS rehabilitation. These promising results merit further long term BCI studies to investigate the effect of MS progression on classification performance.<\/jats:p>","DOI":"10.1088\/1741-2552\/adaa1d","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T22:54:03Z","timestamp":1736895243000},"page":"016012","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Decoding imagined movement in people with multiple sclerosis for brain\u2013computer interface translation"],"prefix":"10.1088","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3010-8541","authenticated-orcid":true,"given":"John S","family":"Russo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas A","family":"Shiels","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chin-Hsuan Sophie","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sam E","family":"John","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5497-7234","authenticated-orcid":true,"given":"David B","family":"Grayden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"jneadaa1dbib1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/7\/2\/026007","article-title":"Toward a hybrid brain-computer interface based on imagined movement and visual attention","volume":"7","author":"Allison","year":"2010","journal-title":"J. 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