中文版 | English
Title

Continuous Bimanual Trajectory Decoding of Coordinated Movement from EEG Signals

Author
Publication Years
2022
DOI
Source Title
ISSN
2168-2208
EISSN
2168-2208
VolumePPIssue: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
SUSTech Authorship
First
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"]
WOS Research Area
Computer Science ; Mathematical & Computational Biology ; Medical Informatics
WOS Subject
Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS Accession No
WOS:000894943300024
Publisher
Data Source
IEEE
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9963543
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/414562
DepartmentDepartment 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 AffilicationDepartment of Biomedical Engineering
First Author's First AffilicationDepartment 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.
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.
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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