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RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.

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Title: RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.
Authors: Zhao Y; School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, China., He D; School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China., Ren F; School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China., Xia Q; School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China., Xu L; College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China., Xie G; School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, China., Zhang X; School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, China., Yang R; School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, China., Zou S; School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, China., Jiang B; School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China.
Source: PloS one [PLoS One] 2026 Apr 22; Vol. 21 (4), pp. e0347671. Date of Electronic Publication: 2026 Apr 22 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.
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  Data: <searchLink fieldCode="AU" term="%22Zhao+Y%22">Zhao Y</searchLink>; School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22He+D%22">He D</searchLink>; School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Ren+F%22">Ren F</searchLink>; School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Xia+Q%22">Xia Q</searchLink>; School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Xu+L%22">Xu L</searchLink>; College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Xie+G%22">Xie G</searchLink>; School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Zhang+X%22">Zhang X</searchLink>; School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Yang+R%22">Yang R</searchLink>; School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Zou+S%22">Zou S</searchLink>; School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Jiang+B%22">Jiang B</searchLink>; School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China.
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  Data: <searchLink fieldCode="JN" term="%22101285081%22">PloS one</searchLink> [PLoS One] 2026 Apr 22; Vol. 21 (4), pp. e0347671. <i>Date of Electronic Publication: </i>2026 Apr 22 (<i>Print Publication: </i>2026).
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              Text: 2026 Apr 22
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