Motor-Sensory Coupled Learning for Motor Imagery Decoding

Brain-Computer Interface (BCI) technology has significant potential for advancing stroke rehabilitation by promoting motor recovery by decoding motor intentions from electroencephalogram (EEG) signals. However, the practical application of BCI in rehabilitation faces several challenges, particularly in decoding accuracy. This limitation often stems from an overemphasis on motor imagery signals, while sensory components, which are crucial for effective motor function recovery, are frequently overlooked. In this paper, we propose a novel framework to enhance BCI performance by integrating both sensory and motor modalities through a motor-sensory coupled learning approach. The model leverages EEG data induced by both motor imagery (MI) and tactile sensation (TS), using adversarial training to capture the coupled features of these two domains. By incorporating reliable sensory signals, the proposed approach aims to improve the robustness and accuracy of motor imagery decoding, offering particular benefits for stroke patients with impaired motor rhythms. Experimental results from BCI-naive subjects show a significant improvement in classification accuracy compared to traditional motor imagery-only models, suggesting that this approach holds promise as a potential solution for stroke rehabilitation. These findings indicate that integrating sensory signals into BCI systems could lead to more effective rehabilitation strategies, paving the way for the development of more robust and adaptive BCI technologies in the future.