Application Prescription of Brain-Computer Interface Based on Motor Imagery in Post-Stroke Dysfunction
Year: 2025
Type: article
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.
Source: Rehabilitation Medicine
Authors: Shanshan JIANG, Zhiqing QIU, Tingting YOU, Xinyu FU, Guanzhou CHEN, Haoda LI, Boyuan LIANG, Jiani YU, Haining OU
Language: English
Subfield: Rehabilitation
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4411109912