脑电图
计算机科学
卷积神经网络
模式识别(心理学)
人工智能
图形
邻接矩阵
理论计算机科学
心理学
精神科
作者
Yue Gao,Xiangling Fu,Tianxiong Ouyang,Yi Wang
出处
期刊:IEEE Signal Processing Letters
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:29: 1574-1578
被引量:32
标识
DOI:10.1109/lsp.2022.3179946
摘要
Graph networks are naturally suitable for modeling multi-channel features of EEG signals. However, the existing study that attempts to utilize graph-based neural networks for EEG-based emotion recognition doesn't take the spatio-temporal redundancy of EEG features and differences in brain topology into account. In this paper, we propose EEG-GCN, a paradigm that adopts spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition. With spatio-temporal attention mechanism employed, EEG-GCN can adaptively capture significant sequential segments and spatial location information in EEG signals. Meanwhile, a self-adaptive brain network adjacency matrix is designed to quantify the connection strength between the channels, in which way to represent the diverse activation patterns under different emotion scenarios. Additionally, we propose a multi-view EEG-based emotion recognition method, which effectively integrates the diverse features of EEG signals. Extensive experiments conducted on two benchmark datasets SEED and DEAP demonstrate that our proposed method outperforms other representative methods from both single and multiple views.
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