DAVG-ViT: a domain-adaptive Vision Transformer with GA-BiLSTM for EEG emotion recognition

EEG-based emotion recognition has attracted considerable research interest due to its non-invasive characteristics and superior temporal resolution. Challenges still remain in modeling global dependencies, cross-channel interactions, and cross-domain generalization. This study proposes a novel model called DAVG-ViT (a Domain-Adaptive Vision Transformer with GA-BiLSTM for EEG Emotion Recognition). It enhances global modeling using Adapters, captures cross-channel relationships and bidirectional temporal dynamics via Group Attention and BiLSTM, and reduces distribution shift through a gradient reversal layer-based adversarial domain adaptation mechanism. Experiments on SEED, SEED-IV, and DEAP show superior performance, achieving accuracies of 99.37% on SEED, 95.91% on SEED-IV, and 98.95% for arousal and 98.47% for valence on DEAP.