Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks

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dc.contributor.authorO. Yeon Kwon
dc.contributor.authorMin Ho Lee
dc.contributor.authorCuntai Guan
dc.contributor.authorSeong Whan Lee
dc.date.accessioned2025-08-20T04:05:20Z
dc.date.available2025-08-20T04:05:20Z
dc.date.issued2020-01-01
dc.description.abstractLarge EEG motor imagery database (54 subjects, 21,600 trials). A subject-independent deep CNN using spectral–spatial embeddings and fusion outperforms subject-dependent models.en
dc.identifier.citationKwon, O.Y.; Lee, M.H.; Guan, C.; Lee, S.W. (2020). IEEE Trans. Neural Netw. Learn. Syst., 31(10):3839–3852. https://doi.org/10.1109/TNNLS.2019.2946869en
dc.identifier.doi10.1109/TNNLS.2019.2946869
dc.identifier.urihttps://doi.org/10.1109/TNNLS.2019.2946869
dc.identifier.urihttps://nur.nu.edu.kz/handle/123456789/9672
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systemsen
dc.rightsOpen accessen
dc.sourceIEEE Transactions on Neural Networks and Learning Systems, 31(10), 3839–3852, (2020)en
dc.subjectBCIen
dc.subjectEEGen
dc.subjectdeep learningen
dc.subjectsubject-independent classificationen
dc.titleSubject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networksen
dc.typeJournal Articleen

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