Brain–Computer Interface Training Based on Brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis. (February 2022)

• Record Type: Journal Article
• Authors: Nojima, Ippei; Sugata, Hisato; Takeuchi, Hiroki; Mima, Tatsuya
• Is Part Of: Neurorehabilitation & neural repair. Volume 36:Number 2(2022)
• Page Start: 83
• Page End: 96
• Publication Date: 2022-02
• Subjects: Brain–computer interface -- stroke -- brain activity -- motor function; Nervous system -- Diseases -- Patients -- Rehabilitation -- Periodicals; Brain damage -- Patients -- Rehabilitation -- Periodicals; Spinal cord -- Wounds and injuries -- Patients -- Rehabilitation -- Periodicals; Nervous system -- Regeneration -- Periodicals; Neuroplasticity -- Periodicals; 616.804305
• DOI: 10.1177/15459683211062895
• Languages: English
• ISSNs: 1545-9683

Abstract

Background: Brain–computer interface (BCI) is a procedure involving brain activity in which neural status is provided to the participants for self-regulation. The current review aims to evaluate the effect sizes of clinical studies investigating the use of BCI-based rehabilitation interventions in restoring upper extremity function and effective methods to detect brain activity for motor recovery. Methods: A computerized search of MEDLINE, CENTRAL, Web of Science, and PEDro was performed to identify relevant articles. We selected clinical trials that used BCI-based training for post-stroke patients and provided motor assessment scores before and after the intervention. The pooled standardized mean differences of BCI-based training were calculated using the random-effects model. Results: We initially identified 655 potentially relevant articles; finally, 16 articles fulfilled the inclusion criteria, involving 382 participants. A significant effect of neurofeedback intervention for the paretic upper limb was observed (standardized mean difference = .48, [.16-.80], P = .006). However, the effect estimates were moderately heterogeneous among the studies ( I 2 = 45%, P = .03). Subgroup analysis of the method of measurement of brain activity indicated the effectiveness of the algorithm focusing on sensorimotor rhythm. Conclusion: This meta-analysis suggested that BCI-based training was superior to conventional interventions for motor recovery of the upper limbs in patients with stroke. However, the results are not conclusive because of a high risk of bias and a large degree of heterogeneity due to the differences in the BCI interventions and the participants; therefore, further studies involving larger cohorts are required to confirm these results.