Causal Graph Convolutional Neural Network for Emotion Recognition

Graph convolutional neural network (GCNN)-based methods have been widely used in electroencephalogram (EEG)-related works due to their advantages of considering the symmetrical connections of brain regions. However, the current GCNN-based methods do not fully explore other correlations between EEG channels. Many studies have proved that definite causal connections exist between brain regions. Therefore, this article proposes a causal GCNN (CGCNN) using the Granger causality (GC) test to calculate interchannel interactions. First, we consider causal relations between EEG channels and construct an asymmetric causal graph with direction. Then, we adopt depthwise separable convolution to extract emotional features from multichannel EEG signals. Experiments carried out on SEED and SEED-IV show that CGCNN has the ability to represent the causal information flow in different emotional states, and improve the classification accuracy to 93.36% on SEED and 75.48% on SEED-IV, respectively. The results outperform other existing methods, indicating that GC is more effective in revealing the correlations between EEG channels in emotion recognition.