Spatio-temporal graph Bert network for EEG emotion recognition

Abstract

EEG data presents a topological structure in spatial. In order to effectively capture the temporal and spatial characteristics of brain data, this paper proposes a Spatio-temporal Graph Bert network (STGB) and applies it to the emotion recognition of Electroencephalogram (EEG) signals. The STGB network learns the EEG features from the spatial and the temporal domains respectively. In the spatial domain, the adjacency matrix is constructed to model the graph of EEG signals, and then the spatial domain features of EEG signals are extracted by using the Graph Bert network through the steps of subgraph partitioning, node embedding, node feature updating based on attention mechanism and node clustering. In the temporal domain, the spatial domain features of EEG signals obtained from each period are connected by the Long Short-Term Memory network (LSTM) to learn the temporal correlation of the EEG signals, so as to complete the EEG emotion recognition task. Experiments on SEED dataset and DEAP dataset prove that STGB can complete the learning of EEG features more comprehensively and accurately, and achieve a higher emotion recognition rate.