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Front Neurosci. 2017 Jul 20;11:400. doi: 10.3389/fnins.2017.00400. eCollection 2017.

Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial.

Author information

1
Research Institute of Translational Medicine, Pirogov Russian National Research Medical UniversityMoscow, Russia.
2
Laboratory of Mathematical Neurobiology of Learning of Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of SciencesMoscow, Russia.
3
Department of Neurorehabilitation and Physiotherapy of Research Center of Neurology, Russian Academy of Medical SciencesMoscow, Russia.
4
Department of Neurology, Vladimirsky Moscow Regional Research Clinical InstituteMoscow, Russia.
5
Medical Faculty, Pirogov Russian National Research Medical UniversityMoscow, Russia.
6
Research Institute of Cerebrovascular Pathology and Stroke, Pirogov Russian National Research Medical UniversityMoscow, Russia.

Abstract

Repeated use of brain-computer interfaces (BCIs) providing contingent sensory feedback of brain activity was recently proposed as a rehabilitation approach to restore motor function after stroke or spinal cord lesions. However, there are only a few clinical studies that investigate feasibility and effectiveness of such an approach. Here we report on a placebo-controlled, multicenter clinical trial that investigated whether stroke survivors with severe upper limb (UL) paralysis benefit from 10 BCI training sessions each lasting up to 40 min. A total of 74 patients participated: median time since stroke is 8 months, 25 and 75% quartiles [3.0; 13.0]; median severity of UL paralysis is 4.5 points [0.0; 30.0] as measured by the Action Research Arm Test, ARAT, and 19.5 points [11.0; 40.0] as measured by the Fugl-Meyer Motor Assessment, FMMA. Patients in the BCI group (n = 55) performed motor imagery of opening their affected hand. Motor imagery-related brain electroencephalographic activity was translated into contingent hand exoskeleton-driven opening movements of the affected hand. In a control group (n = 19), hand exoskeleton-driven opening movements of the affected hand were independent of brain electroencephalographic activity. Evaluation of the UL clinical assessments indicated that both groups improved, but only the BCI group showed an improvement in the ARAT's grasp score from 0 [0.0; 14.0] to 3.0 [0.0; 15.0] points (p < 0.01) and pinch scores from 0.0 [0.0; 7.0] to 1.0 [0.0; 12.0] points (p < 0.01). Upon training completion, 21.8% and 36.4% of the patients in the BCI group improved their ARAT and FMMA scores respectively. The corresponding numbers for the control group were 5.1% (ARAT) and 15.8% (FMMA). These results suggests that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Both maximum and mean values of the percentage of successfully decoded imagery-related EEG activity, were higher than chance level. A correlation between the classification accuracy and the improvement in the upper extremity function was found. An improvement of motor function was found for patients with different duration, severity and location of the stroke.

KEYWORDS:

ClinicalTrials.gov; brain-computer interface; exoskeleton; identifier: NCT02325947; motor imagery; paresis; rehabilitation; stroke

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