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

Among all solutions of emotion recognition tasks, electroencephalo-gram (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. MDAGCN 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.