Application Prescription of Brain-Computer Interface Based on Motor Imagery in Post-Stroke Dysfunction

Brain-computer interface (BCI) establishes a direct communication pathway between the brain and external devices, bypassing the peripheral neuromuscular system. This technology has attracted increasing attention in neurorehabilitation research. Particularly, motor imagery-based BCI (MI-BCI) has demonstrated therapeutic potential in post-stroke rehabilitation targeting motor dysfunction, cognitive impairment, and swallowing disorders. However, the lack of standardized treatment protocols significantly hampers clinical efficacy and widespread implementation of MI-BCI interventions. This review examines existing application protocols of MI-BCI in treating post-stroke upper/lower limb motor dysfunction, cognitive impairment, and dysphagia, with emphasis on the impact of treatment frequency, total intervention duration, and single-session time allocation on rehabilitation outcomes. Furthermore, it explores non-invasive interventions to enhance MI-BCI efficacy, such as visual/auditory feedback, proprioceptive and tactile feedback, virtual reality feedback, and non-invasive brain stimulation techniques. Current limitations in MI-BCI-based stroke rehabilitation include a lack of standardized protocols for parameter settings (e.g., treatment frequency, total course duration, single-session duration) and augmenting therapeutic effects, and technical challenges in signal processing. Future directions should prioritize optimizing application protocols to reduce variability in treatment outcomes, improving system standardization and stability, thereby providing a reference for MI-BCI clinical translation in stroke rehabilitation.

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Publication Details

Journal
Rehabilitation Medicine
Published
2025-04-01
DOI
https://doi.org/10.3724/sp.j.1329.2025.02015
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
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article

Application Prescription of Brain-Computer Interface Based on Motor Imagery in Post-Stroke Dysfunction

Shanshan Jiang, Xinyu Fu, G.T.Y. Chen, Haining Ou et al.
Rehabilitation Medicine
EEG and Brain-Computer Interfaces
article

Application Prescription of Brain-Computer Interface Based on Motor Imagery in Post-Stroke Dysfunction

Shanshan Jiang, Xinyu Fu, G.T.Y. Chen, Haining Ou, Haoda Li, Zhiwei Qiu, Tingting You, Jinming Yu, Bailin Liang
article en

Abstract

Brain-computer interface (BCI) establishes a direct communication pathway between the brain and external devices, bypassing the peripheral neuromuscular system. This technology has attracted increasing attention in neurorehabilitation research. Particularly, motor imagery-based BCI (MI-BCI) has demonstrated therapeutic potential in post-stroke rehabilitation targeting motor dysfunction, cognitive impairment, and swallowing disorders. However, the lack of standardized treatment protocols significantly hampers clinical efficacy and widespread implementation of MI-BCI interventions. This review examines existing application protocols of MI-BCI in treating post-stroke upper/lower limb motor dysfunction, cognitive impairment, and dysphagia, with emphasis on the impact of treatment frequency, total intervention duration, and single-session time allocation on rehabilitation outcomes. Furthermore, it explores non-invasive interventions to enhance MI-BCI efficacy, such as visual/auditory feedback, proprioceptive and tactile feedback, virtual reality feedback, and non-invasive brain stimulation techniques. Current limitations in MI-BCI-based stroke rehabilitation include a lack of standardized protocols for parameter settings (e.g., treatment frequency, total course duration, single-session duration) and augmenting therapeutic effects, and technical challenges in signal processing. Future directions should prioritize optimizing application protocols to reduce variability in treatment outcomes, improving system standardization and stability, thereby providing a reference for MI-BCI clinical translation in stroke rehabilitation.

Rehabilitation MedicineVol. 35(2)
Gender equality
Openalex Percentile: Top 88%
EEG and Brain-Computer Interfaces
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