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Advancing Brain-Computer Interface Closed-Loop Systems for Neurorehabilitation: Systematic Review of AI and Machine

Christopher Williams1, Fahim Islam Anik2, Md Mehedi Hasan3

  • 1Department of Computer Science, Troy University, Troy, AL, United States.

JMIR Biomedical Engineering
|November 5, 2025
PubMed
Summary

AI and machine learning enhance brain-computer interface (BCI) systems for neurological monitoring. Addressing challenges in these systems is key for advancing Alzheimer disease and related dementias care.

Keywords:
AIArtificial IntelligenceBrain-Computer InterfaceMachine LearningPRISMAbiomedical signal processingclosed-loop systemscognitive monitoringhealthcare technologyhuman-computer interactionneurorehabilitationreal-time monitoring

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Brain-computer interface (BCI) closed-loop systems show promise for neurorehabilitation and cognitive assessment.
  • Real-time, noninvasive monitoring is crucial for neurological disorders like Alzheimer disease and related dementias (AD/ADRD).
  • Artificial intelligence (AI) and machine learning (ML) interpret neural signals, but signal noise and privacy are challenges.

Purpose of the Study:

  • Investigate AI and ML's role in enhancing BCI closed-loop systems for healthcare.
  • Analyze methods, assess AI/ML effectiveness, identify challenges, and propose a framework for AD/ADRD monitoring.
  • Provide a comprehensive overview of AI-driven BCIs in neurological healthcare.

Main Methods:

  • Systematic literature review (2019-2024) following PRISMA guidelines.
  • Searched PubMed, IEEE, ACM, Scopus for BCI, AI, and AD/ADRD related studies.
  • Analyzed 18 selected papers for methods, algorithms, limitations, and solutions.

Main Results:

  • ML techniques like transfer learning, SVMs, and CNNs improve BCI performance and cognitive state monitoring.
  • Identified challenges include long calibration, computational costs, data security, and signal variability.
  • Emerging solutions involve better sensors, efficient protocols, and advanced AI decoding models for real-time alerts.

Conclusions:

  • AI/ML integrated BCI closed-loop systems advance neurological healthcare, especially for AD/ADRD.
  • Addressing data accuracy, security, and scalability is vital for clinical adoption.
  • Future research should refine AI models, improve real-time processing, and enhance user accessibility for personalized care.