Abstract: With the rapid development of human-computer interaction in computer aided field, EEG has become the main means of emotion recognition.Meanwhile, graph network has attracted wide attention due to its excellent ability to represent topological data.To further improve the representation performance of graph network on multi-channel EEG signals, in this paper, conside-ring the sparsity and infrequency of EEG signals, a self-adaptive brain graph convolutional network with spatiotemporal attention (SABGCN-ST) is proposed.The method solves the sparsity of emotion via the spatiotemporal attention mechanism and explores the functional connections between different electrode channels via the self-adaptive brain network topological adjacent matrix.Finally, the feature learning of graph structure is operated via graph convolution, and the emotion is predicted.Extensive experiments conduct on two benchmark datasets DEAP and SEED prove that SABGCN-ST has a significant advantage in accuracy compared with baseline models, and the average accuracy of SABGCN-ST reaches 84.91%.

Key words: Deep learning, Electroencephalogram, Emotion recognition, Graph convolutional neural network, Self-adaptive adjacent matrix, Spatio-temporal attention mechanism