Global Context MambaVision for EEG-based Emotion Recognition

Emotion recognition tasks based on physiological signals require the simultaneous capture of local features and global correlations of these signals. Although transformer-based models are widely used due to their superior ability to integrate information, their quadratic computational complexity limits their efficiency in processing large-scale or high-resolution data. Recently, state space models (SSM) with efficient hardware-aware designs have demonstrated significant potential in modeling long sequences. However, existing SSMs face limitations in processing global information due to window constraints. Therefore, this paper introduces a novel Global Context (GC) MambaVision model, which combines the linear time complexity advantage of SSMs with a new type of local-global attention mechanism. GC MambaVision maintains high computational efficiency in emotion recognition tasks while providing a more comprehensive understanding of the dynamic changes in local and global emotional states. Experimental results on the DEAP and SEED-V datasets show that GC MambaVision achieves superior performance compared to current state-of-the-art models, with accuracies reaching 98.62% and 85.88%, respectively.