Brain–Computer Interface for Neurorehabilitation of Upper Limb After Stroke

Brain–Computer Interface for Neurorehabilitation of Upper Limb After Stroke
Title:
Brain–Computer Interface for Neurorehabilitation of Upper Limb After Stroke
Other Titles:
Proceedings of the IEEE
Publication Date:
12 May 2015
Citation:
Ang, K. K., & Guan, C. (2015). Brain-Computer Interface for Neurorehabilitation of Upper Limb After Stroke. Proc. IEEE, 103(6), 944-953.
Abstract:
Current rehabilitation therapies for stroke rely on Physical Practice (PP) by the patients. Motor Imagery (MI), the imagination of movements without physical action, presents an alternate neuro-rehabilitation for stroke patients without relying on residue movements. However, MI is an endogenous mental process that is not physically observable. Recently, advances in Brain-Computer Interface (BCI) technology have enabled the objective detection of MI that spearheaded this alternate neuro-rehabilitation for stroke. In this review, we present 2 strategies of using BCI for neuro-rehabilitation after stroke: detecting MI to trigger a feedback, and detecting MI with a robot to provide concomitant MI and PP. We also present 3 randomized control trials (RCTs) that employed these 2 strategies for upper limb rehabilitation. A total of 125 chronic stroke patients were screened over 6 years. The BCI screening revealed that 103 (82%) patients can use EEG-based BCI, and 75 (60%) performed well with accuracies above 70%. A total of 67 patients were recruited to complete one of the 3 RCTs ranging from 2 to 6 weeks of which 26 patients, who underwent BCI neuro-rehabilitation that employed these 2 strategies, had significant motor improvement of 4.5 measured by Fugl-Meyer Motor Assessment of the upper extremity. Hence the results demonstrate clinical efficacy of using BCI as an alternate neuro-rehabilitation for stroke.
License type:
PublisherCopyrights
Funding Info:
Science and Engineering Research Council of A*STAR (Agency for Science, Technology and Research).
Description:
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
0018-9219
1558-2256
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