Clinical Experience of Post-Stroke Rehabilitation with the Use Of Hand Exoskeleton Controlled by Brain-Computer Interface
Aim - to evaluate the efficiency of the motor recovery rehabilitation procedure with the use of hand exoskeleton controlled by the brain-computer interface (BCI).
Materials and methods. 60 post-stroke patients participated in the study. 46 patients had ischemic stroke and 14 had hemorrhagic stroke. 42 patients of the main experimental group were trained in kinesthetic motor imagery using hand exoskeleton controlled by BCI, 18 patients of the control group carried out the imitating procedure. Exoskeleton - BCI system consists of encephalograph NVX52 («Medical Computer Systems», Russia), personal computer and hand exoskeleton («Android Technique», Russia). Motor functions were estimated by neurological scales ARAT and Fugl-Meyer. Results were statistically analyzed by Mann-Whitney, Wilcoxon and χ² tests, Spearman's correlation and RM-ANOVA using Statsoft Statistica v. 6.0.
Results. It is shown that post-stroke patients are able to control BCI with the same efficiency as healthy subjects, regardless of the duration, severity and localization of the disease. Ten days of BCI training significantly improved patients’ motor functions according to neurological scales ARAT and Fugl-Meyer. Improvement was mainly provided by the small movements of the hand. According to several sections of neurological scales, improvement in the main group is significantly higher than in the control group. However, according to general scores, statistically significant difference between two groups was not observed.
Conclusion. It is shown that the rehabilitation procedure using hand exsoskeleton controlled by BCI significantly improves motor functions of the paretic arm regardless of the duration, severity and localization of the disease. Increase of the training duration enhances the rehabilitation efficiency.
1. Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. Lancet Neurol. 2009; 8 (8): 741–754. doi: 10.1016/S1474-4422(09)70150-4
2. Pollock A, Farmer SE, Brady MC, Langhorne P, Mead GE, Mehrholz J, et al. Interventions for improving upper limb function after stroke. Cochrane Database Syst Rev. 2014 Nov 12; 11:CD010820. doi: 10.1002/14651858.CD010820.pub2.
3. Frolov AA, Husek D, Silchenko AV, Tintera J, Rydlo J. The changes in the hemodynamic activity of the brain during motor imagery training with the use of brain-computer interface. Human Physiology. 2016; 42 (1): 1-12. doi: 10.1134/S0362119716010084
4. Mokienko OA, Lyukmanov RK, Chernikova LA, Suponeva NA, Piradov MA, Frolov AA. Brain–computer interface: The first experience of clinical use in Russia. Human Physiology. 2016; 42 (1): 24-31. doi: 10.1134/S0362119716010126
5. Frolov AA, Mokienko OA, Lukmanov RKh et al. Preliminary results of a controlled study of BCI-exoskeleton technology efficacy in patients with poststroke arm paresis. Vestnik RGMU. 2016; (2): 17-25. (in Russ.).
6. Frolov A, Husek D, Bobrov P. Comparison of four classification methods for brain–computer interface. Neural Network World. 2011; 21 (2): 101–115.
7. Ramos-Murguialday A, Broetz D, Rea M, Laer L, Yilmaz O, Brasil FL, et al. Brain–machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol. 2013 Jul; 74 (1): 100–108. doi: 10.1002/ana.23879
8. Ang KK, Chua KS, Phua KS, Wang C, Chin ZY, Kuah CW, et al. A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke. Clinical EEG and neuroscience. 2015; 46 (4): 310-20. doi: 10.1177/1550059414522229
Frolov AA, Mokienko OA, Biryukova EV, Bobrov PD, Lukmanov RKh, Kondur AA, Dzhalagonya IZ. Clinical Experience of Post-Stroke Rehabilitation with the Use of Hand Exoskeleton Сontrolled by Brain-Computer Interface. Science & Innovations in Medicine. 2016(3):56-61.
Send an online application form to the publicationSend