Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition

EEG-TransNet, a new transformer-based architecture for EEG emotion recognition, has been introduced, featuring a preprocessing module utilizing ResNet and wavelet denoising, a Local Self-Attention Block for regional feature extraction, and a Fuzzy-Attention Synchronous Transformer (FAST) for modeling spatiotemporal dependencies. Extensive evaluations on the BETA, SEED, and DepEEG datasets demonstrate superior classification accuracy and robustness compared to existing methods, with ablation studies highlighting the effectiveness of the Local Self-Attention Block and the computational efficiency achieved through depthwise separable convolutions in the decoder. This model's ability to generalize across subjects with minimal performance variation positions it as a valuable tool for practitioners in EEG-based emotion recognition and brain activity analysis.