Title | Continuous Bimanual Trajectory Decoding of Coordinated Movement from EEG Signals |
Author | |
Publication Years | 2022
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DOI | |
Source Title | |
ISSN | 2168-2208
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EISSN | 2168-2208
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Volume | PPIssue:99Pages:1-11 |
Abstract | While many voluntary movements involve bimanual coordination, few attempts have been made to simultaneously decode the trajectory of bimanual movements from electroencephalogram (EEG) signals. In this study, we proposed a novel bimanual brain-computer interface (BCI) paradigm to reconstruct the continuous trajectory of both hands during coordinated movements from EEG. The protocol required human subjects to complete a bimanual reaching task to the left, middle, or right target while EEG data were collected. A multi-task deep learning model combining the EEGNet and long short-term memory network (LSTM) was proposed to decode bimanual trajectories, including position and velocity. Decoding performance was evaluated in terms of the correlation coefficient (CC) and normalized root mean square error (NRMSE) between decoded and real trajectories. Experimental results from 13 human subjects showed that the grand-averaged combined CC values achieved 0.54 and 0.42 for position and velocity decoding, respectively. The corresponding combined NRMSE values were 0.22 and 0.23. Both CC and NRMSE were significantly superior to the chance level (p < 0.05). Comparative experiments also indicated that the proposed model significantly outperformed some other commonly-used methods in terms of CC and NRMSE for continuous trajectory decoding. These findings demonstrated the feasibility of simultaneously decoding bimanual trajectory from EEG, indicating the potential of bimanual control for coordinated tasks. |
Keywords | |
URL | [Source Record] |
Indexed By | |
Language | English
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SUSTech Authorship | First
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Funding Project | National Natural Science Foundation of China[62273173]
; Shenzhen Science and Technology Program["JCYJ20220530113811027","JCYJ20220818103602004","JCYJ20210324104203010"]
; National Key Research and Development Program of China["2022YFF1202500","2022YFF1202502"]
; Shenzhen Key Laboratory of Smart Healthcare Engineering[ZDSYS20200811144003009]
; Guangdong Provincial Key Laboratory of Advanced Biomaterials[2022B1212010003]
; Guangdong Province["2020ZDZX3001","2019ZT08Y191"]
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WOS Research Area | Computer Science
; Mathematical & Computational Biology
; Medical Informatics
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WOS Subject | Computer Science, Information Systems
; Computer Science, Interdisciplinary Applications
; Mathematical & Computational Biology
; Medical Informatics
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WOS Accession No | WOS:000894943300024
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Publisher | |
Data Source | IEEE
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PDF url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9963543 |
Citation statistics |
Cited Times [WOS]:2
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Document Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/414562 |
Department | Department of Biomedical Engineering |
Affiliation | Shenzhen Key Laboratory of Smart Healthcare Engineering and Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China |
First Author Affilication | Department of Biomedical Engineering |
First Author's First Affilication | Department of Biomedical Engineering |
Recommended Citation GB/T 7714 |
Yi-Feng Chen,Ruiqi Fu,Junde Wu,et al. Continuous Bimanual Trajectory Decoding of Coordinated Movement from EEG Signals[J]. IEEE Journal of Biomedical and Health Informatics,2022,PP(99):1-11.
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APA |
Yi-Feng Chen.,Ruiqi Fu.,Junde Wu.,Jongbin Song.,Rui Ma.,...&Mingming Zhang.(2022).Continuous Bimanual Trajectory Decoding of Coordinated Movement from EEG Signals.IEEE Journal of Biomedical and Health Informatics,PP(99),1-11.
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MLA |
Yi-Feng Chen,et al."Continuous Bimanual Trajectory Decoding of Coordinated Movement from EEG Signals".IEEE Journal of Biomedical and Health Informatics PP.99(2022):1-11.
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