Multi-modal supervised domain adaptation with a multi-level alignment strategy and consistent decision boundaries for cross-subject emotion recognition from EEG and eye movement signals

Multi-modal emotion recognition systems from Electroencephalogram (EEG) and eye tracking signals have overcome the limitation of incomplete information expressed by a single modality, leveraging the complementarity of multiple modal information. However, the applicability of these systems is still r...

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Veröffentlicht in:	Knowledge-based systems Jg. 315; S. 113238
Hauptverfasser:	Jiménez-Guarneros, Magdiel, Fuentes-Pineda, Gibran
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 22.04.2025
Schlagworte:	Deep learning Electroencephalogram Eye tracking Multi-modal emotion recognition Multi-modal supervised domain adaptation Deep learning Multi-modal emotion recognition Multi-modal supervised domain adaptation Electroencephalogram Eye tracking
ISSN:	0950-7051
Online-Zugang:	Volltext
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