Domain Adaptive Algorithm Based on Multi-Manifold Embedded Distributed Alignment for Brain-Computer Interfaces

The use of transfer learning in brain-computer interfaces (BCIs) has potential applications. As electroencephalogram (EEG) signals vary among different paradigms and subjects, existing EEG transfer learning algorithms mainly focus on the alignment of the original space. They may not discover hidden...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics Jg. 27; H. 1; S. 296 - 307
Hauptverfasser:	Gao, Yunyuan, Liu, Yici, She, Qingshan, Zhang, Jianhai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive algorithms Algorithms Alignment Brain-computer interface Brain-Computer Interfaces Classification Covariance matrices Covariance matrix Datasets Divergence domain adaptive Domains EEG Electroencephalography Electroencephalography - methods Embedding Feature extraction Geometry Human-computer interface Humans Interfaces Machine learning Manifolds Manifolds (mathematics) Riemann manifold Signal Processing, Computer-Assisted subspace learning transfer learn-ing Transfer learning
ISSN:	2168-2194, 2168-2208, 2168-2208
Online-Zugang:	Volltext
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