Continuous Bimanual Trajectory Decoding of Coordinated Movement from EEG Signals

Yi-Feng Chen; Ruiqi Fu; Junde Wu; Jongbin Song; Rui Ma; Yi-chuan Jiang; Mingming Zhang

2022

10.1109/JBHI.2022.3224506

IEEE Journal of Biomedical and Health Informatics   Impact Factor & Quartile

2168-2208

2168-2208

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.

Brain-computer interface (BCI) bimanual trajectory coordinated movement deep learning electroencephalogram (EEG)

SCI

English

First

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"]

Computer Science ; Mathematical & Computational Biology ; Medical Informatics

Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics

WOS:000894943300024

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

IEEE

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

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.

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.

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.