Adaptive Dual-Space Network With Multigraph Fusion for EEG-Based Emotion Recognition

Most of the work on electroencephalogram (EEG)-based emotion recognition aims to extract the distinguishing features from high-dimensional EEG signals, ignoring the complementarity of information between EEG latent space and graph space. Furthermore, the influence of brain connectivity on emotions encompasses both physical structure and functional connectivity, which may have varying degrees of importance for different individuals. To address these issues, this article introduces an adaptive dual-space network (ADS-Net) with multigraph fusion aimed at capturing more comprehensive information by integrating dual-space representations. Specifically, ADS-Net models the spatial correlation of EEG channels in graph topological space, while exploring long-range dependencies and frequency relationships from EEG data in latent space. Subsequently, these representations are adaptively combined through an innovative gated fusion approach to extract complementary corepresentations. Moreover, drawing on the principles of brain connectivity theory, the proposed method constructs a multigraph to indicate the associativity of EEG channels. To further capture individual differences, an adaptive multigraph fusion mechanism is developed for the dynamic integration of physical and functional connectivity graphs. When compared to state-of-the-art methods, the superior experimental results underscore the effectiveness and broad applicability of the proposed method.