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This paper is in the following e-collection/theme issue:

Digital Health Reviews (3724) Neurology and Neurosciences (390) Decision Support for Health Professionals (2268) Cognitive and Neurorehabilitation (442) mHealth for Rehabilitation (569) Brain-Machine Interfaces (62)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/79132, first published .
Brain scan on computer monitor in a medical office, showing brain activity data.

Efficacy of Brain-Computer Interface Therapy for Upper Limb Rehabilitation in Chronic Stroke: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Efficacy of Brain-Computer Interface Therapy for Upper Limb Rehabilitation in Chronic Stroke: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Authors of this article:

HongJie Chen1, 2 Author Orcid Image ;   GuoJun Yun1 Author Orcid Image
Article Authors Cited by (11) Tweetations (2) Metrics

Journals

  1. Fedorov M, Repin D, Voshev D, Klevtsova O, Nikitin E, Shchegolev P, Ignatyev S, Nesutulov A, Timofeeva M, Elizarova P, Tkachenko A. Prospects for the application of brain-computer interfaces in healthcare: socioeconomic effects and risks. A review. Russian Journal of Cardiology 2026;31(2S):6902 View
  2. Hu S, Wang F, Gao X, Zhi Y, Kim D. Effects of Brain-Computer Interface-Controlled Hand Robot Training on Post-Stroke Recovery of Upper Limb Motor Functions: A Meta-Analysis of Dose-Matched Randomized Controlled Trials. Brain Sciences 2026;16(6):552 View
  3. Balasubramanian S. A structural causal model for robot-assisted upper-limb neurorehabilitation. Frontiers in Rehabilitation Sciences 2026;7 View
  4. Pan Y, Huang Y, Bao M, Sun X. Evolution of brain-computer interface technologies for stroke rehabilitation: a bibliometric integration of neural decoding and functional recovery (2016–2025). Frontiers in Neuroscience 2026;20 View
  5. Wang J, Yuan Y, Xu H, Luan G. Scientific and Technological Developments in Brain-Computer Interfaces: Dual Bibliometric Analysis of Articles and Patents. JMIR Rehabilitation and Assistive Technologies 2026;13:e95902 View
  6. Petrunina E, Ermakov D, Skvortsov M, Drejzin V, Filist S, Shatalova O. Biotechnical system for managing upper limb rehabilitation based on monitoring an indirect criterion of biotechnical resonance. Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering 2026;16(2):54 View
  7. Tao R, Duan C, Zhang Y. Closed-loop brain-computer interfaces for post-stroke sensorimotor loop restoration. Frontiers in Neuroscience 2026;20 View
  8. Xu J, Gao Y, Xie S, Jiang W, Zhang H, Qiu L, Jiang M, Zhou L. Comparative Effectiveness of Noninvasive Brain-Computer Interface–Based Interventions for Upper Limb Rehabilitation in Poststroke Hemiplegia: Systematic Review and Network Meta-Analysis of Randomized Controlled Trials. Journal of Medical Internet Research 2026;28:e92940 View
  9. Kim R, Lee H, Kang N. Advancing Brain–Computer Interface Systems for Stroke Motor Recovery: An Umbrella Review of Meta-Analyses. Symmetry 2026;18(9):1484 View
  10. Watanabe G, Shimizu Y, Takehara K, Mataki Y, Kubota S, Hada Y. Feasibility Evaluation of a Novel Bilateral Hand and Finger Training Device in Patients with Hand Dysfunction: A Preliminary Study. Healthcare 2026;14(18):2915 View
  11. Alom M, Rahaman M, Hossain M, Mondal C, Shakib M, Habib M, Hasan M, Ali A. Towards trustworthy brain stroke diagnosis using a lightweight explainable deep learning framework for CT imaging. Scientific Reports 2026;16(1) View