Multi-View Hierarchical Attention Graph Convolutional Network with Domain Adaptation for EEG Emotion Recognition

EEG signals have emerged as a potent modality for emotion recognition owing to their direct correspondence to brain activity. However, the considerable inter-individual variability of EEG signals and the intricacy of their spatial structural features pose significant challenges. To address these challenges, we introduce the Multi-View Hierarchical Attention Graph Convolutional Network (MAGCN) accompanied by Domain Adaptation, a novel architecture that effectively combines multi-view adjacency matrices with a hierarchical attention mechanism and domain adaptation for robust EEG-based emotion recognition. The MAGCN model leverages both functional and spatial connections within EEG data, facilitating dynamic graph convolution that readily adapts to the variability of emotional states. Our domain discriminator, inspired by adversarial learning, guarantees the extraction of domain-invariant features, subsequently augmenting the model's generalisation across subjects. Experiments on the SEED and SEED-IV datasets attest to the MAGCN model's superior performance over existing methods.