An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped Mammal
Brain-computer interfaces (BCI) acquire electroencephalogram (EEG) signals to effectively address postoperative motor dysfunction in stroke patients by discerning their motor intentions during significant movements. Traditionally, noninvasive BCIs have been constrained by limitations in their usage...
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10972026/ |
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| author | Sining Li Gan Liu Fan Feng Ziqing Chang Wenyu Li Feng Duan |
| author_facet | Sining Li Gan Liu Fan Feng Ziqing Chang Wenyu Li Feng Duan |
| author_sort | Sining Li |
| collection | DOAJ |
| description | Brain-computer interfaces (BCI) acquire electroencephalogram (EEG) signals to effectively address postoperative motor dysfunction in stroke patients by discerning their motor intentions during significant movements. Traditionally, noninvasive BCIs have been constrained by limitations in their usage environments; whereas, invasive BCIs damage neural permanently. Therefore, we proposed a novel interventional BCI, in which electrodes are implanted along the veins into the brain to acquire intracerebral EEG signals without an open craniotomy. We collect EEG signals from the primary motor cortex in the superior sagittal sinus of sheep during three different significant movements: laying down; standing; and walking. The first three month data are used to train the neural network, and The fourth month of data were used to validate. The deep learning model achieved an 86% accuracy rate in classifying motion states in validation. Furthermore, the results of the power spectral density (PSD) show that the signal power in the main frequency band did not decrease over a period of five months, which demonstrates that the interventional BCI has the ability to effectively capture EEG signals over long periods of time. |
| format | Article |
| id | doaj-art-23981ec3912f421197bb8e36e8e543a3 |
| institution | OA Journals |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-23981ec3912f421197bb8e36e8e543a32025-08-20T01:48:16ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01331633164210.1109/TNSRE.2025.356292210972026An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped MammalSining Li0https://orcid.org/0000-0001-5514-2821Gan Liu1https://orcid.org/0000-0003-1686-3321Fan Feng2Ziqing Chang3Wenyu Li4https://orcid.org/0000-0001-8846-5301Feng Duan5https://orcid.org/0000-0002-2179-2460Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation, Nankai University, Tianjin, ChinaTianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation, Nankai University, Tianjin, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, ChinaTianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation, Nankai University, Tianjin, ChinaTianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation, Nankai University, Tianjin, ChinaTianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation, Nankai University, Tianjin, ChinaBrain-computer interfaces (BCI) acquire electroencephalogram (EEG) signals to effectively address postoperative motor dysfunction in stroke patients by discerning their motor intentions during significant movements. Traditionally, noninvasive BCIs have been constrained by limitations in their usage environments; whereas, invasive BCIs damage neural permanently. Therefore, we proposed a novel interventional BCI, in which electrodes are implanted along the veins into the brain to acquire intracerebral EEG signals without an open craniotomy. We collect EEG signals from the primary motor cortex in the superior sagittal sinus of sheep during three different significant movements: laying down; standing; and walking. The first three month data are used to train the neural network, and The fourth month of data were used to validate. The deep learning model achieved an 86% accuracy rate in classifying motion states in validation. Furthermore, the results of the power spectral density (PSD) show that the signal power in the main frequency band did not decrease over a period of five months, which demonstrates that the interventional BCI has the ability to effectively capture EEG signals over long periods of time.https://ieeexplore.ieee.org/document/10972026/Interventional BCIEEGmotion classification |
| spellingShingle | Sining Li Gan Liu Fan Feng Ziqing Chang Wenyu Li Feng Duan An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped Mammal IEEE Transactions on Neural Systems and Rehabilitation Engineering Interventional BCI EEG motion classification |
| title | An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped Mammal |
| title_full | An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped Mammal |
| title_fullStr | An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped Mammal |
| title_full_unstemmed | An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped Mammal |
| title_short | An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped Mammal |
| title_sort | interventional brain computer interface for long term eeg collection and motion classification of a quadruped mammal |
| topic | Interventional BCI EEG motion classification |
| url | https://ieeexplore.ieee.org/document/10972026/ |
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