康复学报 (Jan 2025)
Application of EEG-Based Brain-Computer Interface Technology in Stroke Rehabilitation
Abstract
Patients with stroke often suffer from motor, cognitive and speech function disorders, which seriously affect their quality of life. As an innovative technology that combines real-time assessment and rehabilitation training, electroencephalogram (EEG)-based brain-computer interface (BCI) technology has shown great potential in stroke rehabilitation. This study reviews the overview of EEG-BCI technology (definition and classification of BCI, basic characteristics of EEG signals, and types of EEG-BCI paradigms), its application in stroke rehabilitation, and its shortcomings and prospects. The EEG-BCI paradigms include motor imagery (MI), event-related potentials (ERP), steady-state evoked potentials (SSEP), and hybrid paradigms (hBCI), etc. The applications of EEG-BCI technology in stroke rehabilitation include motor function rehabilitation (upper limb movement and hand function, lower limb movement function, gait function, etc), cognitive function rehabilitation, and speech function rehabilitation. The shortcomings of the application include large signal noise, low spatial resolution, and insufficient personalized schemes. By optimizing deep learning algorithms, establishing personalized treatment systems, ethical norms for multimodal fusion, and phased clinical translation strategies, EEG-BCI technology is expected to provide more precise and safe rehabilitation plans for stroke patients.
