Abstract
Emotion recognition based on EEG has attracted widespread research interest in the field of brain-computer interfaces. To extract EEG intra- and inter-channel features and find discriminative representations for EEG emotion recognition, we propose EEG-Multilayer Perceptron (EEG-MLP) architecture. EEG-MLP is completely composed of MLPs and mainly consists of two modules, one is a temporal mixer that captures intra-channel (temporal) information, and the other is a channel mixer that captures inter-channel information. The two modules learn knowledge in a parallel manner, and then their outputs are fused to extract global information and classify EEG emotions. We conduct extensive experiments on DEAP dataset. EEG-MLP is first compared with five inter-channel interaction models (related to CNN or GCN) to verify its effectiveness. Then, five other models with similar architecture to EEG-MLP were also contrasted. Experimental results show that EEG-MLP achieves the best performance among the above methods, with accuracies of 94.87% and 95.32% in the valence and arousal dimensions, respectively. In addition, it has a strong discrimination ability for complex categories, and has low requirements for storage resources.