A Multi-Domain Adaptive Graph Convolutional Network for EEG-based Emotion Recognition

Among all solutions of emotion recognition tasks, electroencephalogram (EEG) is a very effective tool and has received broad attention from researchers. In addition, information across multimedia in EEG often provides a more complete picture of emotions. However, few of the existing studies concurrently incorporate EEG information from temporal domain, frequency domain and functional brain connectivity. In this paper, we propose a Multi-Domain Adaptive Graph Convolutional Network (MD-AGCN), fusing the knowledge of both the frequency domain and the temporal domain to fully utilize the complementary information of EEG signals. MD-AGCN also considers the topology of EEG channels by combining the inter-channel correlations with the intra-channel information, from which the functional brain connectivity can be learned in an adaptive manner. Extensive experimental results demonstrate that our model exceeds state-of-the-art methods in most experimental settings. At the same time, the results show that MD-AGCN could extract complementary domain information and exploit channel relationships for EEG-based emotion recognition effectively.