Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study
Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this stu...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Behavioural Neurology |
| Online Access: | http://dx.doi.org/10.1155/2017/3435686 |
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| _version_ | 1849692752784130048 |
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| author | Yue Li Shaomin Zhang Yile Jin Bangyu Cai Marco Controzzi Junming Zhu Jianmin Zhang Xiaoxiang Zheng |
| author_facet | Yue Li Shaomin Zhang Yile Jin Bangyu Cai Marco Controzzi Junming Zhu Jianmin Zhang Xiaoxiang Zheng |
| author_sort | Yue Li |
| collection | DOAJ |
| description | Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures. |
| format | Article |
| id | doaj-art-92864578de2a4b1bb4782cab4d0e636e |
| institution | DOAJ |
| issn | 0953-4180 1875-8584 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Behavioural Neurology |
| spelling | doaj-art-92864578de2a4b1bb4782cab4d0e636e2025-08-20T03:20:37ZengWileyBehavioural Neurology0953-41801875-85842017-01-01201710.1155/2017/34356863435686Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot StudyYue Li0Shaomin Zhang1Yile Jin2Bangyu Cai3Marco Controzzi4Junming Zhu5Jianmin Zhang6Xiaoxiang Zheng7Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaQiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaQiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaQiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaThe Biorobotics Institute, Scuola Superiore Sant’Anna, Pisa, ItalyKey Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Hangzhou, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Zhejiang University, Hangzhou, ChinaQiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaElectrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures.http://dx.doi.org/10.1155/2017/3435686 |
| spellingShingle | Yue Li Shaomin Zhang Yile Jin Bangyu Cai Marco Controzzi Junming Zhu Jianmin Zhang Xiaoxiang Zheng Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study Behavioural Neurology |
| title | Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study |
| title_full | Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study |
| title_fullStr | Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study |
| title_full_unstemmed | Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study |
| title_short | Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study |
| title_sort | gesture decoding using ecog signals from human sensorimotor cortex a pilot study |
| url | http://dx.doi.org/10.1155/2017/3435686 |
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