Bi-Branch Vision Transformer Network for EEG Emotion Recognition
Wei Lu, Tien-Ping Tan, Hua Ma
Abstract
Electroencephalogram (EEG) signals have emerged as an important tool for emotion research due to their objective reflection of real emotional states. Deep learning-based EEG emotion classification algorithms have made preliminary progress, but existing models struggle with capturing long-range dependence and integrating temporal, frequency, and spatial domain features to limit their classification ability. To address these challenges, this study proposes a Bi-branch Vision Transformer-based EEG emotion recognition model, Bi-ViTNet, that integrates spatial-temporal and spatial-frequency feature representations. Specifically, Bi-ViTNet is composed of spatial-frequency feature extraction branch and spatial-temporal feature extraction branch, which can fuse spatial-frequency-temporal features in a unified framework. Each branch is composed of Linear Embedding and Transformer Encoder, which is used to extract spatial-frequency features and spatial-temporal features. Finally, fusion and classification are performed by the Fusion and Classification layer. Experiments on SEED and SEED-IV datasets demonstrate that Bi-ViTNet outperforms state-of-the-art baselines.