A Spectral-Temporal Refined Attention Network via Contrastive Mutual Learning for Closed-Loop Motor Imagery BCI

The motor imagery (MI) based brain–computer interface (BCI) holds broad application prospects in human–machine interaction. However, current MI recognition approaches primarily utilize complex attention modules for higher recognition accuracy, consequently hindering real-time BCI implementation. Furthermore, existing methods often overlook inter-subject variability, leading to inadequate generalization of model. Additionally, traditional BCI systems lack closed-loop feedback from the machine to the brain. To address these limitations, we develop a novel closed-loop motor imagery BCI system, which encompasses a spectral-temporal refined attention network via contrastive mutual learning (STRA-CML) and a brain-controlled perceived hand exoskeleton. Specifically, we first design a spectral temporal refined attention block to capture the most discriminative spectral and temporal features. Second, we investigate a contrastive mutual learning strategy incorporating supervised-contrastive learning to enhance the generalization of our STRA-CML. Finally, a brain–machine closed-loop interaction platform based on perceived hand exoskeleton is developed to validate the feasibility of the proposed STRA-CML and provide kinesthetic and visual feedback synchronized with MI. Competitive experimental results on two public datasets and a self-collected dataset demonstrate the effectiveness of our STRA-CML, indicating that our STRA-CML achieves superior classification performance of 83.89% on BCI IV 2a dataset, 86.93% on BCI IV 2b dataset, and 82.79% on self-collected dataset.