Enhancing Motor Imagery Decoding for Stroke Patients Using Data Augmentation and Transfer Learning.

This study proposes an innovative WalkBCI real-time motor imagery brain-computer interface system to solve the problems of time-consuming model calibration and low EEG decoding accuracy in stroke patients because of individual differences. WalkBCI integrates generative adversarial networks and transfer-learning techniques to generate motor imagery feature data via RM-GAN using the resting data of target subjects, which shortens the time of calibration data acquisition and reduces patient fatigue. Meanwhile, the system enhances classification performance by combining source domain data during transfer learning optimization. The system is evaluated in both offline and online experiments to assess its practicality and stability. The study results show that WalkBCI outperformed traditional methods on the stroke patient dataset. In the offline experiments, the model's accuracy, precision, recall, and F 1-score of the model increased by 3.3%, 2.6%, 3.8%, and 3.4%, respectively. In online experiments, the system maintained stable classification performance with a maximum accuracy of 72.5%, while reducing calibration time by 58%. WalkBCI effectively lowered subjects' fatigue, and the comparison experiments revealed average reductions of 7.5 points in FS-14 scores and 74.05 in frontal theta/beta values. This study offers an efficient and stable solution for real-time EEG decoding, particularly well-suited for motor-imagery tasks in stroke patients.