Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients.

Journal: Scientific data
Published Date: Aug 20, 2025

Abstract

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.

Authors

  • Chongwen Zuo
    Department of Rehabilitation Medicine, Air Force Medical Center of Chinese PLA, Beijing, China.
  • Yi Yin
    China School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, China.
  • HaoChong Wang
    Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
  • Zhiyang Zheng
    H. Hilton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, GA USA.
  • Xiaoyan Ma
    Department of Ultrasound, Guangdong Women and Children' Hospital, Guangzhou, P. R. China.
  • Yuan Yang
    The Ministry of Education Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Northwestern Polytechnical University, No. 127, Youyi Road (West), Xi'an 710072, China.
  • Jue Wang
    State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau SAR, China.
  • Shan Wang
    Department of Echocardiography & Noninvasive Cardiology Laboratory, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, Chengdu, Sichuan, 610047, China.
  • Zi-Gang Huang
    Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, and Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
  • Chaoqun Ye
    Department of Rehabilitation Medicine, Air Force Medical Center of Chinese PLA, Beijing, China. yecq2023@foxmail.com.

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