Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury
IntroductionRehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention de...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1532099/full |
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| author | Chenyang Li Chenyang Li Yuchen Xu Tao Feng Tao Feng Minmin Wang Minmin Wang Minmin Wang Xiaomei Zhang Li Zhang Li Zhang Ruidong Cheng Weihai Chen Weihai Chen Weidong Chen Weidong Chen Shaomin Zhang Shaomin Zhang Shaomin Zhang Shaomin Zhang |
| author_facet | Chenyang Li Chenyang Li Yuchen Xu Tao Feng Tao Feng Minmin Wang Minmin Wang Minmin Wang Xiaomei Zhang Li Zhang Li Zhang Ruidong Cheng Weihai Chen Weihai Chen Weidong Chen Weidong Chen Shaomin Zhang Shaomin Zhang Shaomin Zhang Shaomin Zhang |
| author_sort | Chenyang Li |
| collection | DOAJ |
| description | IntroductionRehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG–EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices.MethodsEight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG–EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme.ResultsAmong the three functional connectivity methods investigated—coherence, Pearson correlation coefficient and mutual information (MI)—the MI-based EEG–EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p > 0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p > 0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications.ConclusionOur results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems. |
| format | Article |
| id | doaj-art-37cb0fa2fabc4524b2bfd3e0d1feace2 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj-art-37cb0fa2fabc4524b2bfd3e0d1feace22025-01-24T07:13:37ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-01-011910.3389/fnins.2025.15320991532099Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injuryChenyang Li0Chenyang Li1Yuchen Xu2Tao Feng3Tao Feng4Minmin Wang5Minmin Wang6Minmin Wang7Xiaomei Zhang8Li Zhang9Li Zhang10Ruidong Cheng11Weihai Chen12Weihai Chen13Weidong Chen14Weidong Chen15Shaomin Zhang16Shaomin Zhang17Shaomin Zhang18Shaomin Zhang19Key Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaDepartment of Biomedical Engineering, Zhejiang University, Hangzhou, ChinaCenter of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou, ChinaKey Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaDepartment of Biomedical Engineering, Zhejiang University, Hangzhou, ChinaKey Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaDepartment of Biomedical Engineering, Zhejiang University, Hangzhou, ChinaWestlake Institute for Optoelectronics, Westlake University, Hangzhou, ChinaThe First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaThe First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Rehabilitation Medicine, Center for Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital Hangzhou Medical College), Hangzhou, ChinaDepartment of Rehabilitation Medicine, Center for Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital Hangzhou Medical College), Hangzhou, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, ChinaHangzhou Innovation Institute, Beihang University, Hangzhou, Zhejiang, ChinaKey Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaDepartment of Biomedical Engineering, Zhejiang University, Hangzhou, ChinaKey Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaDepartment of Biomedical Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China0The MOE Frontier Science Center for Brain Science & Brain-machine Integration, Zhejiang University, Hangzhou, ChinaIntroductionRehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG–EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices.MethodsEight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG–EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme.ResultsAmong the three functional connectivity methods investigated—coherence, Pearson correlation coefficient and mutual information (MI)—the MI-based EEG–EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p > 0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p > 0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications.ConclusionOur results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems.https://www.frontiersin.org/articles/10.3389/fnins.2025.1532099/fullmultimodalhuman-machine interfaceelectroencephalographysurface electromyographymuscular fatiguepre-movement intention detection |
| spellingShingle | Chenyang Li Chenyang Li Yuchen Xu Tao Feng Tao Feng Minmin Wang Minmin Wang Minmin Wang Xiaomei Zhang Li Zhang Li Zhang Ruidong Cheng Weihai Chen Weihai Chen Weidong Chen Weidong Chen Shaomin Zhang Shaomin Zhang Shaomin Zhang Shaomin Zhang Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury Frontiers in Neuroscience multimodal human-machine interface electroencephalography surface electromyography muscular fatigue pre-movement intention detection |
| title | Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury |
| title_full | Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury |
| title_fullStr | Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury |
| title_full_unstemmed | Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury |
| title_short | Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury |
| title_sort | fusion of eeg and emg signals for detecting pre movement intention of sitting and standing in healthy individuals and patients with spinal cord injury |
| topic | multimodal human-machine interface electroencephalography surface electromyography muscular fatigue pre-movement intention detection |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1532099/full |
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