[1]LI Jingcong,PAN Weijian,LIN Zhenyuan,et al.Emotional EEG signal recognition method using multi-path graph attention network[J].CAAI Transactions on Intelligent Systems,2022,17(3):531-539.[doi:10.11992/tis.202107003]
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Emotional EEG signal recognition method using multi-path graph attention network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 3 Page number: 531-539 Column: 学术论文—机器感知与模式识别 Public date: 2022-05-05
Title:
Emotional EEG signal recognition method using multi-path graph attention network
Author(s):
LI Jingcong;  PAN Weijian;  LIN Zhenyuan;  CHEN Xichang;  PAN Jiahui
Software College, South China Normal University, Foshan 528200, China
Keywords:
emotion recognition; cross-subject; graph convolutional neural network; graph attention network; electroencephalogram signal; brain-computer interface; neural network; deep learning
CLC:
TP391
DOI:
10.11992/tis.202107003
Abstract:
Emotion is a generalization of subjective cognition produced by the brain. Brain signal decoding technology can effectively study human emotions and related cognitive behaviors more objectively. This paper proposes a multi-path graph attention networks (MPGAT) method for electroencephalogram (EEG) emotion recognition based on graph attention networks. This method uses convolutional layers to extract the time domain features of EEG signals and the features of each frequency band by mapping EEG signal channels; moreover, it uses the graph attention network to further capture the local features of emotional EEG signals and the internal functional relationships among the brain regions; constructing a better representation of EEG signals later. The average accuracy of MPGAT’s cross-subject emotion recognition in the SEED and SEED-IV datasets were 86.03% and 72.71%, respectively, and the average cross-subject accuracy rates of the valence and arousal dimensions in the DREAMER dataset were 76.35% and 75.46%, respectively, meeting and partially exceeding the performance of the most advanced EEG emotion recognition methods. The EEG signal processing method proposed in this paper can provide novel technical means for the scientific research of emotional cognition and emotional brain-computer interface system.
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