Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements
Brain–Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/7/452 |
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| author | Shuangling Ma Zijie Situ Xiaobo Peng Zhangyang Li Ying Huang |
| author_facet | Shuangling Ma Zijie Situ Xiaobo Peng Zhangyang Li Ying Huang |
| author_sort | Shuangling Ma |
| collection | DOAJ |
| description | Brain–Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy, restricting their practical application. This study focuses on rehabilitation training scenarios, aiming to capture the motor intentions of patients with partial or complete motor impairments (such as stroke survivors) and provide feedforward control commands for exoskeletons. This study developed an EEG acquisition protocol specifically for use with lower-limb rehabilitation motor imagery (MI). It systematically explored preprocessing techniques, feature extraction strategies, and multi-classification algorithms for multi-task MI-EEG signals. A novel 3D EEG convolutional neural network (3D EEG-CNN) that integrates time/frequency features is proposed. Evaluations on a self-collected dataset demonstrated that the proposed model achieved a peak classification accuracy of 66.32%, substantially outperforming conventional approaches and demonstrating notable progress in the multi-class classification of lower-limb motor imagery tasks. |
| format | Article |
| id | doaj-art-7df102ff6d524f74b1fe2207063ebd30 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-7df102ff6d524f74b1fe2207063ebd302025-08-20T03:58:26ZengMDPI AGBiomimetics2313-76732025-07-0110745210.3390/biomimetics10070452Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation MovementsShuangling Ma0Zijie Situ1Xiaobo Peng2Zhangyang Li3Ying Huang4College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaShenzhen Key Laboratory of Marine Bioresources and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, ChinaBrain–Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy, restricting their practical application. This study focuses on rehabilitation training scenarios, aiming to capture the motor intentions of patients with partial or complete motor impairments (such as stroke survivors) and provide feedforward control commands for exoskeletons. This study developed an EEG acquisition protocol specifically for use with lower-limb rehabilitation motor imagery (MI). It systematically explored preprocessing techniques, feature extraction strategies, and multi-classification algorithms for multi-task MI-EEG signals. A novel 3D EEG convolutional neural network (3D EEG-CNN) that integrates time/frequency features is proposed. Evaluations on a self-collected dataset demonstrated that the proposed model achieved a peak classification accuracy of 66.32%, substantially outperforming conventional approaches and demonstrating notable progress in the multi-class classification of lower-limb motor imagery tasks.https://www.mdpi.com/2313-7673/10/7/452MI-EEG signalsfour-class classificationcommon spatial patternconvolutional neural network3D EEG-CNN |
| spellingShingle | Shuangling Ma Zijie Situ Xiaobo Peng Zhangyang Li Ying Huang Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements Biomimetics MI-EEG signals four-class classification common spatial pattern convolutional neural network 3D EEG-CNN |
| title | Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements |
| title_full | Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements |
| title_fullStr | Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements |
| title_full_unstemmed | Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements |
| title_short | Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements |
| title_sort | multi class classification methods for eeg signals of lower limb rehabilitation movements |
| topic | MI-EEG signals four-class classification common spatial pattern convolutional neural network 3D EEG-CNN |
| url | https://www.mdpi.com/2313-7673/10/7/452 |
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