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

TY - JOUR

T1 - A Spectral-Temporal Refined Attention Network via Contrastive Mutual Learning for Closed-Loop Motor Imagery BCI

AU - Yan, Weidong

AU - Liu, Jingyu

AU - Zhang, Zeyu

AU - Zhang, Qinge

AU - Li, Yang

N1 - Publisher Copyright: © 2014 IEEE.

PY - 2026

Y1 - 2026

N2 - 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.

AB - 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.

KW - Braina-computer interface (BCI)

KW - closed-loop braina-machine interaction

KW - contrastive mutual learning (CML)

KW - motor imagery

KW - perceived hand exoskeleton

KW - spectral-temporal refined attention (STRA)

UR - https://www.scopus.com/pages/publications/105017698638

U2 - 10.1109/TCSS.2025.3607894

DO - 10.1109/TCSS.2025.3607894

M3 - Article

AN - SCOPUS:105017698638

SN - 2329-924X

VL - 13

SP - 1614

EP - 1626

JO - IEEE Transactions on Computational Social Systems

JF - IEEE Transactions on Computational Social Systems

IS - 2

ER -