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

Biomedical Engineering Reviews (44) Clinical Information and Decision Making (3826) Dementia and Cognitive Decline (900) Cognitive and Neurorehabilitation (443) Machine Learning (3288) Artificial Intelligence (4914) Brain-Machine Interfaces (63)

Published on in Vol 10 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72218, first published .
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Advancing Brain-Computer Interface Closed-Loop Systems for Neurorehabilitation: Systematic Review of AI and Machine Learning Innovations in Biomedical Engineering

Advancing Brain-Computer Interface Closed-Loop Systems for Neurorehabilitation: Systematic Review of AI and Machine Learning Innovations in Biomedical Engineering

Authors of this article:

Christopher Williams1 Author Orcid Image ;   Fahim Islam Anik2 Author Orcid Image ;   Md Mehedi Hasan3 Author Orcid Image ;   Juan Rodriguez-Cardenas3 Author Orcid Image ;   Anushka Chowdhury4 Author Orcid Image ;   Shirley Tian3 Author Orcid Image ;   Selena He3 Author Orcid Image ;   Nazmus Sakib3 Author Orcid Image
Article Authors Cited by (15) Tweetations (2) Metrics

Journals

  1. Liu Z, Liu J, Zhang P, Xu X. PEDOT:PSS as a Bio-Solid Electrolyte Interphase for Neural Interfaces: From Molecular Design to Interfacial Intelligence. Polymers 2025;18(1):20 View
  2. Gan W, Xiao M, Chen S, Mohammed T, Song X. From “Scanning” to “Shaping”: An Ethical Risk and Governance Mechanism Study of AI Empowered Brain Computer Interfaces Based on the Reference of the Large-scale Game SOMA. Scientific Research Bulletin 2025;2(4):80 View
  3. Yu Z, Mulholland A, Huang T, Liu Q. Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities. Journal of Medical Internet Research 2026;28:e85414 View
  4. Mongiardini E, Belardinelli P. Closing the Loop in Neuromodulation: A Review of Machine Learning Approaches for EEG-Guided Transcranial Magnetic Stimulation. Algorithms 2026;19(4):323 View
  5. Chuck Easttom . Machine Learning and Brain-Computer Interfaces for Medical and Psychiatric Translation, 2024-2026. Journal of Psychology and Neuroscience 2026 View
  6. Yao Y, XU S, Kim E, Kim E, Wang Y, Kim H, Kim D, Liang J, Shin Y, Kim N. Electroencephalography Functional Network Responses to Immersive Virtual Reality in Alzheimer Disease and Mild Cognitive Impairment: Exploratory Single-Session 3-Group Repeated-Measures Study. JMIR Serious Games 2026;14:e95160 View
  7. Wu H, Yang Y, Wang L, Su H, Li X. The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms. Journal of Evaluation in Clinical Practice 2026;32(6) View
  8. Fu Y, Yan L, Chen X, Pan J, Wang F, Li T, Zhao L, Yang X, Luo R, Xu J, Gu X. A theoretical framework for brain–computer interfaces: decodability, performance limits, and closed-loop adaptation. Frontiers in Human Neuroscience 2026;20 View
  9. Wang Y, Wen B, Wu Q, Pan Q, Qu Q, Qiao J, Xu J. A novel multi-modal fusion framework based on simultaneous EEG-fNIRS for the classification of working memory loads. Cognitive Neurodynamics 2026;20(1) View
  10. Yue Z, Li H, Zheng D, Min R, Liu Q, Ding H, He F, Leal-Junior A, Li X. Integrated intelligent optoelectronic systems for neural interfacing: from optical stimulation to signal readout. Intelligent Opto-Electronics 2026:260013 View

Books/Policy Documents

  1. Lu Y, Zhang Z, Peng G. Biosensors - Fundamentals, Applications, and Artificial Intelligence Integration [Working Title]. View
  2. Das P, Das B. Brain-Computer Interfaces for Neurorehabilitation. View
  3. Aggarwal A, Pradhan S, Sharma A. Brain-Computer Interfaces for Neurorehabilitation. View
  4. Mehra S, Chakraborty A, Samadhiya P. Legal Implications of Brain-Computer Interfaces in Modern Society. View

Conference Proceedings

  1. J R, Sangameswaran B, T B, V A, M R, M S. 2026 8th International Conference on Inventive Material Science and Applications (ICIMA). Real Time Brain to Spine Neural Bypass System for Paralyzed Patients using AI and Neuromodulation View