REVIEW ARTICLE
Brain-computer interfaces usage in neurorehabilitation of patients after stroke – systematic review
1
Student Scientific Association of Neurology, Wroclaw Medical University, Poland
2
Faculty of Fundamental Problems of Technology, Department of Biomedical Engineering, Wroclaw University of Science and Technology, Poland
3
Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Poland
4
Department of Neurology, University Center of Neurology and Neurosurgery, Wroclaw Medical University, Poland
Submission date: 2025-11-30
Final revision date: 2026-02-24
Acceptance date: 2026-02-25
Publication date: 2026-09-23
Corresponding author
Sylwiusz Kontek
Student Scientific Association of Neurology, Wroclaw Medical University, Wrocław, Poland
Student Scientific Association of Neurology, Wroclaw Medical University, Wrocław, Poland
Issue Rehabil. Orthop. Neurophysiol. Sport Promot. 2026;54(1):41-54
KEYWORDS
TOPICS
ABSTRACT
Introduction:
Stroke is a leading cause of long-term disability worldwide, necessitating effective rehabilitation strategies. Currently, neurofeedback and brain-computer interfaces (BCI) based on electroencephalography (EEG) provide promising tools for controlled and precise neurorehabilitation.
Aim:
To assess the therapeutic efficacy of EEG-based BCI usage in the neurorehabilitation process of post-stroke patients. The assessment focuses on the impact of motor functions and the induction of neuroplasticity.
Materials and methods:
A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search across five major databases resulted in the final inclusion of 32 articles.
Results:
Analysis revealed a diversity of BCI modalities, primarily utilizing motor imagery combined with virtual reality, functional electrical stimulation, and robotic exoskeletons to close the sensorimotor loop. Procedural parameters varied significantly: session durations ranged from brief tasks to one-hour protocols. The total number of sessions are also varied widely, from pilot studies to extended regimens of up to 80 sessions, averaging approximately 30 sessions. Regarding signal acquisition, most studies utilized high-density EEG setups (typically 20 or 32 leads). The cohorts predominantly included patients with chronic ischemic stroke, although healthy individuals were often recruited for system validation. Therapeutic effects were confirmed by significant improvements in clinical scales, particularly the Fugl-Meyer Assessment and neurophysiological markers, such as suppression of the mu rhythm.
Conclusions:
Evidence supports that BCI-related methods significantly improve clinical outcomes in stroke survivors. However, the extent of positive neurological impact depends on the specific BCI modality and stroke characteristics. Future research requires standardized protocols to optimize dosage and include underrepresented patient groups.
Stroke is a leading cause of long-term disability worldwide, necessitating effective rehabilitation strategies. Currently, neurofeedback and brain-computer interfaces (BCI) based on electroencephalography (EEG) provide promising tools for controlled and precise neurorehabilitation.
Aim:
To assess the therapeutic efficacy of EEG-based BCI usage in the neurorehabilitation process of post-stroke patients. The assessment focuses on the impact of motor functions and the induction of neuroplasticity.
Materials and methods:
A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search across five major databases resulted in the final inclusion of 32 articles.
Results:
Analysis revealed a diversity of BCI modalities, primarily utilizing motor imagery combined with virtual reality, functional electrical stimulation, and robotic exoskeletons to close the sensorimotor loop. Procedural parameters varied significantly: session durations ranged from brief tasks to one-hour protocols. The total number of sessions are also varied widely, from pilot studies to extended regimens of up to 80 sessions, averaging approximately 30 sessions. Regarding signal acquisition, most studies utilized high-density EEG setups (typically 20 or 32 leads). The cohorts predominantly included patients with chronic ischemic stroke, although healthy individuals were often recruited for system validation. Therapeutic effects were confirmed by significant improvements in clinical scales, particularly the Fugl-Meyer Assessment and neurophysiological markers, such as suppression of the mu rhythm.
Conclusions:
Evidence supports that BCI-related methods significantly improve clinical outcomes in stroke survivors. However, the extent of positive neurological impact depends on the specific BCI modality and stroke characteristics. Future research requires standardized protocols to optimize dosage and include underrepresented patient groups.
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