Design and application of pneumatic rehabilitation glove system based on brain–computer interface.

The article focuses on the development of a pneumatic rehabilitation glove system based on a brain–computer interface (BCI) for hand function rehabilitation in stroke patients. This system integrates ElectroEncephaloGram (EEG) acquisition with a novel EEG processing model called EEGTrans, which uses a transformer to enhance motor imagery (MI) recognition accuracy, enabling the glove to perform specific movements according to the patient's imagined hand motions. Experimental results demonstrate that EEGTrans achieves an accuracy of 87.3%, outperforming existing models, and the pneumatic glove’s flexible design with current loop feedback allows individualized finger control without active limb movement. The study acknowledges limitations in precise individual finger control and outlines future improvements in signal processing, power modules, and dataset diversity to enhance system accuracy and usability for diverse patient populations.