Adaptive Hierarchical Graph Convolutional Network for EEG Emotion Recognition

Abstract

Human emotion is closely related to multiple distributed brain regions, and functional connections exist between the regions. However, how to abstract the region-level information to improve electroencephalograph (EEG) emotion recognition performance has not been well considered. To address this problem, we proposed a novel Adaptive Hierarchical Graph Convolutional Network (AHGCN), which includes the basic channel-level graph of EEG channels and the region-level graph of brain regions. Different from previous methods, we propose an adaptive pooling operation to automatically partition brain regions rather than manually define them. To capture the intrinsic functional connections between the brain regions or EEG channels, we design a gated adaptive graph convolution operation. Besides, we develop a graph unpooling operation to integrate the region-level graph and channel-level graph to extract more discrimination features for classification. Experiments on two widely-used datasets show that our proposed method is superior to many state-of-the-art methods on EEG emotion recognition and could find some interesting combinations of EEG channels.