Abstract

Upper limb dysfunction following stroke is a significant factor affecting patients' daily living abilities, social participation, and quality of life. While traditional rehabilitation therapies can promote motor function recovery to some extent, their efficacy remains limited for patients with moderate to severe upper limb dysfunction. A motor imagery brain-computer interface (MI-BCI) that acquires and decodes EEG signals generated during motor imagery transforms the brain's motor intentions into external feedback, thereby reconstructing the closed-loop pathway of "cortical activation—peripheral feedback—sensory input." It is considered a cutting-edge rehabilitation technology for promoting neuroplasticity and reconstructing motor function. In recent years, multiple randomized controlled trials and systematic reviews have shown that MI-BCI has a positive effect on upper-limb motor function recovery, improvements in daily living abilities, and brain function reorganization after stroke, especially when combined with feedback methods such as robotics and functional electrical stimulation. However, this technology still faces challenges in terms of sample size, standardization of intervention protocols, long-term follow-up, and clinical implementation pathways. This article reviews and analyzes the mechanism of action and current clinical applications of MI-BCI, aiming to provide a theoretical basis and practical reference for the precise rehabilitation of upper-limb dysfunction after stroke.