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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

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Front. Med.    2021, Vol. 15 Issue (5) : 740-749    https://doi.org/10.1007/s11684-020-0794-5
RESEARCH ARTICLE
EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application
Long Chen1,2, Bin Gu1, Zhongpeng Wang1, Lei Zhang2, Minpeng Xu1, Shuang Liu2, Feng He1,2, Dong Ming1,2()
1. Neural Engineering & Rehabilitation Laboratory, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
2. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
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Abstract

Stroke is one of the most serious diseases that threaten human life and health. It is a major cause of death and disability in the clinic. New strategies for motor rehabilitation after stroke are undergoing exploration. We aimed to develop a novel artificial neural rehabilitation system, which integrates brain--computer interface (BCI) and functional electrical stimulation (FES) technologies, for limb motor function recovery after stroke. We conducted clinical trials (including controlled trials) in 32 patients with chronic stroke. Patients were randomly divided into the BCI-FES group and the neuromuscular electrical stimulation (NMES) group. The changes in outcome measures during intervention were compared between groups, and the trends of ERD values based on EEG were analyzed for BCI-FES group. Results showed that the increase in Fugl Meyer Assessment of the Upper Extremity (FMA-UE) and Kendall Manual Muscle Testing (Kendall MMT) scores of the BCI-FES group was significantly higher than that in the sham group, which indicated the practicality and superiority of the BCI-FES system in clinical practice. The change in the laterality coefficient (LC) values based on μ-ERD (ΔLCm-ERD) had high significant positive correlation with the change in FMA-UE(r= 0.6093, P=0.012), which provides theoretical basis for exploring novel objective evaluation methods.
Keywords brain–computer interface      functional electrical stimulation      electroencephalogram      laterality coefficient      chronic stroke     
Corresponding Author(s): Dong Ming   
Just Accepted Date: 18 March 2021   Online First Date: 21 June 2021    Issue Date: 01 November 2021
 Cite this article:   
Long Chen,Bin Gu,Zhongpeng Wang, et al. EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application[J]. Front. Med., 2021, 15(5): 740-749.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-020-0794-5
https://academic.hep.com.cn/fmd/EN/Y2021/V15/I5/740
Fig.1  Procedure of the clinical test with EEG-control FES rehabilitation system. The whole flow includes calibration session (offline) and intervention session (online), and the classifier applied in the online session was established in the offline session.
Fig.2  Probability density distribution of classification decision value for offline calibration session.
Fig.3  The flow chart for patient recruitment.
Demographic data BCI-FES group
(n = 16)		 Control group
(n = 16)		 t P
Sex (female) 2 5 / 1
Injury side (left) 7 7 / 1
Age (year) 59.31 57.93 0.705 0.486
Time (month) 6.27 5.73 -1.324 0.201
FMA-UE 8 2.81 0.654 0.518
MMT (Kendall) 63.6% 66.5% 0.996 0.327
MBI 68.9 62.1 0.802 0.429
Tab.1  Comparison of the general information between groups (n = 32)
Fig.4  Mean changes in the FMA-UE (A), Kendall MMT (B), and MBI scores (C) before and after the whole therapy by intervention group.
Fig.5  Change trends of m-ERD and b-ERD values along with the rehabilitation process.
Variables ?LCm-ERD ?LCb-ERD
?FMA-UE r=0.609, P=0.012 r=0.490, P=0.04
?MMT r= 0.610, P=0.011 r= 0.460, P=0.06
?BMI r= 0.049, P=0.86 r= 0.160, P=0.55
Tab.2  Correlation between ?LCm-ERD /?LCb-ERD and changes of outcome measures
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