Multi-Source Domain Adaptive Adversarial Training Network Emotion Recognition

The variability and dynamic nature of electroencephalography (EEG) signals pose challenges in the analysis and classification of cross-domain data for EEG emotion recognition. To tackle the heterogeneity and distributional inconsistencies inherent in multi-source EEG data, we introduce a Multi-Source Domain Adversarial Training Network (MD-ATN). This approach utilizes a multi-branch feature extraction architecture to capture shared features across multiple source domains, while incorporating a Gradient Reversal Layer (GRL) for adversarial training between feature extractors and domain classifiers to generate domain-invariant generic features. Moreover, our model implements a joint optimization strategy that combines Maximum Mean Discrepancy (MMD) and Distribution Discrepancy Loss (DDL) to enhance domain adaptation. Experimental results demonstrate that MD-ATN outperforms existing methods across SEED datasets. Specifically, the MD-ATN model achieves an average accuracy of 87.1 % in the Cross-Subject task and 89.5% in the Cross-Session task.