{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:30:07Z","timestamp":1773246607214,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"S\u00e3o Paulo Research Foundation","award":["2023\/00640-1"],"award-info":[{"award-number":["2023\/00640-1"]}]},{"name":"S\u00e3o Paulo Research Foundation","award":["88887.595656\/2020-00"],"award-info":[{"award-number":["88887.595656\/2020-00"]}]},{"name":"S\u00e3o Paulo Research Foundation","award":["NSF-2316420"],"award-info":[{"award-number":["NSF-2316420"]}]},{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","award":["2023\/00640-1"],"award-info":[{"award-number":["2023\/00640-1"]}]},{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","award":["88887.595656\/2020-00"],"award-info":[{"award-number":["88887.595656\/2020-00"]}]},{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","award":["NSF-2316420"],"award-info":[{"award-number":["NSF-2316420"]}]},{"name":"US National Science Foundation","award":["2023\/00640-1"],"award-info":[{"award-number":["2023\/00640-1"]}]},{"name":"US National Science Foundation","award":["88887.595656\/2020-00"],"award-info":[{"award-number":["88887.595656\/2020-00"]}]},{"name":"US National Science Foundation","award":["NSF-2316420"],"award-info":[{"award-number":["NSF-2316420"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain\u2013computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.<\/jats:p>","DOI":"10.3390\/s24165428","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T12:58:07Z","timestamp":1724417887000},"page":"5428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Independent Vector Analysis for Feature Extraction in Motor Imagery Classification"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7175-0966","authenticated-orcid":false,"given":"Caroline Pires Alavez","family":"Moraes","sequence":"first","affiliation":[{"name":"Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo Andr\u00e9 09280-560, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8789-8938","authenticated-orcid":false,"given":"Lucas Heck","family":"dos Santos","sequence":"additional","affiliation":[{"name":"Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo Andr\u00e9 09280-560, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5009-3431","authenticated-orcid":false,"given":"Denis Gustavo","family":"Fantinato","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Automation (DCA), Universidade Estadual de Campinas (UNICAMP), Campinas 13083-852, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0924-2036","authenticated-orcid":false,"given":"Aline","family":"Neves","sequence":"additional","affiliation":[{"name":"Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo Andr\u00e9 09280-560, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0594-2796","authenticated-orcid":false,"given":"T\u00fclay","family":"Adali","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County (UMBC), Baltimore, MD 21250, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1113\/jphysiol.2006.125948","article-title":"Brain-Computer Interfaces as new brain output pathways","volume":"579","author":"Wolpaw","year":"2007","journal-title":"J. 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