Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients
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 unde...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05767-2 |
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| author | Chongwen Zuo Yi Yin Haochong Wang Zhiyang Zheng Xiaoyan Ma Yuan Yang Jue Wang Shan Wang Zi-gang Huang Chaoqun Ye |
| author_facet | Chongwen Zuo Yi Yin Haochong Wang Zhiyang Zheng Xiaoyan Ma Yuan Yang Jue Wang Shan Wang Zi-gang Huang Chaoqun Ye |
| author_sort | Chongwen Zuo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a48c1dce1ca24baba9fa292638c7b957 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-a48c1dce1ca24baba9fa292638c7b9572025-08-24T11:07:16ZengNature PortfolioScientific Data2052-44632025-08-0112111310.1038/s41597-025-05767-2Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patientsChongwen Zuo0Yi Yin1Haochong Wang2Zhiyang Zheng3Xiaoyan Ma4Yuan Yang5Jue Wang6Shan Wang7Zi-gang Huang8Chaoqun Ye9Department of Rehabilitation Medicine, Air Force Medical Center of Chinese PLADepartment of Rehabilitation Medicine, Air Force Medical Center of Chinese PLAInstitute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong UniversityDepartment of Rehabilitation Medicine, Air Force Medical Center of Chinese PLAState Grid Intergrated Energy Service Group CO, LTDSport Dapartment, Beihang UniversityInstitute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong UniversityDepartment of Rehabilitation Medicine, Air Force Medical Center of Chinese PLAInstitute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong UniversityDepartment of Rehabilitation Medicine, Air Force Medical Center of Chinese PLAAbstract 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.https://doi.org/10.1038/s41597-025-05767-2 |
| spellingShingle | Chongwen Zuo Yi Yin Haochong Wang Zhiyang Zheng Xiaoyan Ma Yuan Yang Jue Wang Shan Wang Zi-gang Huang Chaoqun Ye Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients Scientific Data |
| title | Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients |
| title_full | Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients |
| title_fullStr | Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients |
| title_full_unstemmed | Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients |
| title_short | Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients |
| title_sort | enhancing classification of a large lower limb motor imagery eeg dataset for bci in knee pain patients |
| url | https://doi.org/10.1038/s41597-025-05767-2 |
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