Zuo Chongwen, Yin Yi, Wang Haochong, Zheng Zhiyang, Ma Xiaoyan, Yang Yuan, Wang Jue, Wang Shan, Huang Zi-Gang, Ye Chaoqun
Department of Rehabilitation Medicine, Air Force Medical Center of Chinese PLA, Beijing, China.
Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Sci Data. 2025 Aug 20;12(1):1451. doi: 10.1038/s41597-025-05767-2.
Chronic knee osteoarthritis pain significantly impacts patients' quality of life and motor function. While motor imagery (MI)-based brain-computer interface (BCI) systems have shown promise in rehabilitation, their application to lower-limb conditions, particularly in pain patients, is underexplored. This study evaluates the feasibility of applying an MI-BCI model to a large dataset of knee pain patients, utilizing a novel deep learning algorithm for signal decoding. This EEG data was collected and analysed from 30 knee pain patients, revealing significant event-related (de)synchronization (ERD/ERS) during MI tasks. Traditional decoding algorithms achieved accuracies of 51.43%, 55.71%, and 76.21%, while the proposed OTFWRGD algorithm reached an average accuracy of 86.41%. This dataset highlights the potential of lower-limb MI in enhancing neural plasticity and offers valuable insights for future MI-BCI applications in lower-limb rehabilitation, especially for patients with knee pain.
慢性膝骨关节炎疼痛严重影响患者的生活质量和运动功能。虽然基于运动想象（MI）的脑机接口（BCI）系统在康复治疗中已显示出前景，但其在下肢疾病，特别是疼痛患者中的应用仍未得到充分探索。本研究评估了将MI-BCI模型应用于大量膝关节疼痛患者数据集的可行性，采用了一种新颖的深度学习算法进行信号解码。从30名膝关节疼痛患者收集并分析了脑电图数据，发现在MI任务期间存在显著的事件相关（去）同步化（ERD/ERS）。传统解码算法的准确率分别为51.43%、55.71%和76.21%，而所提出的OTFWRGD算法的平均准确率达到了86.41%。该数据集突出了下肢MI在增强神经可塑性方面的潜力，并为未来MI-BCI在下肢康复中的应用提供了有价值的见解，特别是对于膝关节疼痛患者。
