Time-Frequency Domain Fusion Transformer for Cross-Subject Motor Imagery Classification

Zijian Xia, Jianfeng Li, Jiahui Pan

Abstract: Brain-Computer Interfaces (BCIs) utilizing motor imagery (MI) have become prevalent, yet challenges in deep learning-based MI classification remain, especially regarding domain shift mitigation in low-channel MI and the integration of multimodal features. This study proposes the Time-Frequency Domain Fusion Transformer (TFDFT), a novel multimodal framework designed to overcome these hurdles. The TFDFT employs an agent attention mechanism to seamlessly integrate time and frequency features across dimensions, bolstering the model’s generalization. To counter domain shifts, we have also introduced the Domain Generalization in Conditional Domain Adversarial Network (DG-CDAN) for low-channel MI across subjects. Experimental results demonstrate that TFDFT achieves state-of-the-art performance, surpassing previous methods in cross-subject MI classification with only three channels, achieving 73.30% and 76.37% cross-subject accuracy on the BCIC IV-2a and IV-2b datasets, respectively. The TFDFT significantly surpasses the constraints of single-modal approaches, offering a robust solution for BCI applications.

External IDs:dblp:conf/icmcs/XiaLP25