EEG-Based Cross-Subject Decoding of Motor Imagery for Affected-Hand Finger Tapping in Chronic Stroke Patients

This study presents a cross-subject electroencephalography (EEG) decoding framework for imagined finger tapping of the affected hand in chronic stroke patients. EEG from 11 participants was recorded using 27 channels during motor imagery of individual fingers. To capture functional connectivity, the weighted phase lag index was computed across six frequency bands. Due to high inter-subject variability, Kruskal-Wallis and Mann-Whitney U tests were used for two-stage feature selection. Selected features were classified using Random Forest, Support Vector Machine, and AdaBoost, with leave-one-subject-out validation. AdaBoost achieved the best performance (39.6% accuracy, 38.7% F1-score), exceeding chance level. Discriminative features were prominent in alpha and delta bands, reflecting activity in prefrontal and parietal-occipital areas. These results indicate that stroke patients retain task-relevant neural patterns, and the proposed method supports interpretable, scalable classification for motor imagery-based rehabilitation.