Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

Traditional electroencephalograph (EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject, which restricts the application of the affective brain computer interface (BCI) in practice. We attempt to use the multi-modal data from the past...

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Veröffentlicht in:	IEEE/CAA journal of automatica sinica Jg. 9; H. 9; S. 1612 - 1626
Hauptverfasser:	Wang, Yixin, Qiu, Shuang, Li, Dan, Du, Changde, Lu, Bao-Liang, He, Huiguang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway Chinese Association of Automation (CAA) 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Brain modeling Calibration Cycle-consistency Data models domain adaptation Domains electroencephalograph (EEG) Electroencephalography Emotion recognition Emotions Human-computer interface Image reconstruction Knowledge management Modal data multi modality Regularization Representations variational autoencoder
ISSN:	2329-9266, 2329-9274
Online-Zugang:	Volltext
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