An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals

Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this article, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract the spatial features that characterize the func...

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Veröffentlicht in:	IEEE transactions on affective computing Jg. 13; H. 3; S. 1528 - 1540
Hauptverfasser:	Du, Xiaobing, Ma, Cuixia, Zhang, Guanhua, Li, Jinyao, Lai, Yu-Kun, Zhao, Guozhen, Deng, Xiaoming, Liu, Yong-Jin, Wang, Hongan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	attention mechanism Brain modeling Channels Data models domain adaptation Domains Electrodes Electroencephalography Emotion recognition Emotions Feature extraction Frequency-domain analysis LSTM Machine learning multichannel EEG
ISSN:	1949-3045, 1949-3045
Online-Zugang:	Volltext
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