Mutual Information Disentanglement Based Domain Adaptation Model for EEG Emotion Recognition

To address the lack of generalized feature representation in cross-domain electroencephalogram emotion recognition, this letter proposes a multi-source domain adaptation model that integrates prototype-based class-level constraints into a mutual information disentanglement mechanism. The model mainly consists of a mutual information disentanglement module that separates domain-related and domain-unrelated features by minimizing mutual information, and a prototype classification module that enhances intra-class compactness and semantic consistency across domains. To align multiple source domains with the target domain, Central Moment Discrepancy is minimized in the prototype space. Unlike prior multi-branch MSDA models, our method uses a unified feature extractor, enhancing scalability. Experiments show our model outperforms existing domain adaptation models and achieves high accuracy and stability.