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Abstract
Stroke patients have symptoms of cerebral functional disturbance that could aggressively impair patient’s physical mobility, such as hand impairments. Although rehabilitation training from external devices is beneficial for hand movement recovery, for initiating motor function restoration purposes, there are still valuable research merits for identifying the side of hands in motion. In this preliminary study, we used an electroencephalogram (EEG) dataset from 8 stroke patients, with each subject conducting 40 EEG trials of left motor attempts and 40 EEG trials of right motor attempts. Then, we proposed a majority vote based EEG classification system for identifying the side in motion. In specific, we extracted 1–50 Hz power spectral features as input for a series of well-known classification models. The predicted labels from these classification models were compared and a majority vote based method was applied, which determined the finalised predicted label. Our experiment results showed that our proposed EEG classification system achieved\(99.83 \pm 0.42 \% \) accuracy,\( 99.98 \pm 0.13\% \) precision,\( 99.66 \pm 0.84 \% \) recall, and\( 99.83 \pm 0.43\% \) f-score, which outperformed the performance of single well-known classification models. Our findings suggest that the superior performance of our proposed majority vote based EEG classification system has the potential for stroke patients’ hand rehabilitation.
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University of Tasmania, Hobart, TAS, 7005, Australia
Xiaotong Gu & Zehong Cao
- Xiaotong Gu
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- Zehong Cao
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Correspondence toZehong Cao.
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Editors and Affiliations
Department of AI, Ping An Life, Shenzhen, China
Haiqin Yang
Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand
Kitsuchart Pasupa
City University of Hong Kong, Kowloon, Hong Kong
Andrew Chi-Sing Leung
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
James T. Kwok
School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
Jonathan H. Chan
The Chinese University of Hong Kong, New Territories, Hong Kong
Irwin King
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Gu, X., Cao, Z. (2020). An EEG Majority Vote Based BCI Classification System for Discrimination of Hand Motor Attempts in Stroke Patients. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_6
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