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. |
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| 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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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0347671 |
