Literature DB >> 31887332

Motor imagery based brain-computer interface control of continuous passive motion for wrist extension recovery in chronic stroke patients.

Rong-Rong Lu1, Mou-Xiong Zheng2, Jie Li3, Tian-Hao Gao1, Xu-Yun Hua2, Gang Liu1, Song-Hua Huang1, Jian-Guang Xu4, Yi Wu5.   

Abstract

Motor recovery of wrist and fingers is still a great challenge for chronic stroke survivors. The present study aimed to verify the efficiency of motor imagery based brain-computer interface (BCI) control of continuous passive motion (CPM) in the recovery of wrist extension due to stroke. An observational study was conducted in 26 chronic stroke patients, aged 49.0 ± 15.4 years, with upper extremity motor impairment. All patients showed no wrist extension recovery. A 24-channel highresolution electroencephalogram (EEG) system was used to acquire cortical signal while they were imagining extension of the affected wrist. Then, 20 sessions of BCI-driven CPM training were carried out for 6 weeks. Primary outcome was the increase of active range of motion (ROM) of the affected wrist from the baseline to final evaluation. Improvement of modified Barthel Index, EEG classification and motor imagery pattern of wrist extension were recorded as secondary outcomes. Twenty-one patients finally passed the EEG screening and completed all the BCI-driven CPM trainings. From baseline to the final evaluation, the increase of active ROM of the affected wrists was (24.05 ± 14.46)˚. The increase of modified Barthel Index was 3.10 ± 4.02 points. But no statistical difference was detected between the baseline and final evaluations (P > 0.05). Both EEG classification and motor imagery pattern improved. The present study demonstrated beneficial outcomes of MI-based BCI control of CPM training in motor recovery of wrist extension using motor imagery signal of brain in chronic stroke patients.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Brain-computer interface; Continuous passive motion; Rehabilitation; Stroke; motor imagery

Mesh:

Year:  2019        PMID: 31887332     DOI: 10.1016/j.neulet.2019.134727

Source DB:  PubMed          Journal:  Neurosci Lett        ISSN: 0304-3940            Impact factor:   3.046


  3 in total

1.  EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training.

Authors:  Gege Zhan; Shugeng Chen; Yanyun Ji; Ying Xu; Zuoting Song; Junkongshuai Wang; Lan Niu; Jianxiong Bin; Xiaoyang Kang; Jie Jia
Journal:  Front Hum Neurosci       Date:  2022-06-27       Impact factor: 3.473

2.  Motor Imagery-Based Brain-Computer Interface Combined with Multimodal Feedback to Promote Upper Limb Motor Function after Stroke: A Preliminary Study.

Authors:  Yi-Qian Hu; Tian-Hao Gao; Jie Li; Jia-Chao Tao; Yu-Long Bai; Rong-Rong Lu
Journal:  Evid Based Complement Alternat Med       Date:  2021-11-03       Impact factor: 2.629

3.  Development of a Low-Cost EEG-Controlled Hand Exoskeleton 3D Printed on Textiles.

Authors:  Rommel S Araujo; Camille R Silva; Severino P N Netto; Edgard Morya; Fabricio L Brasil
Journal:  Front Neurosci       Date:  2021-06-25       Impact factor: 4.677

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.