Causality-Driven Convolutional Manifold Attention Network for Electroencephalogram Signal Decoding

Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of distribution (OOD) scenarios. To address this limitation, the present study prop...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. PP; S. 1 - 12
Hauptverfasser:	Lu, Bin, Chen, Junxiang, Wang, Fuwang, Wen, Guilin, Fu, Rongrong, Hua, Changchun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 27.10.2025
Schlagworte:	Brain modeling Causality Decoding Deep learning EEG Electroencephalography Feature extraction Invariant Representation Manifolds Motors Reliability Riemannian Manifold Semantics Symmetric matrices
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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