EEG Ensemble Learning Framework for Prognostic Prediction of Post-rTMS Motor Recovery in Stroke.

Abstract

Repetitive transcranial magnetic stimulation (rTMS) is a promising neuromodulatory therapy for post-stroke rehabilitation, offering the potential to modulate brain network dynamics. However, its clinical effectiveness remains debated due to the high inter-individual variability in patient responses, largely attributed to heterogeneous patterns of neural reorganization after stroke. In this study, we proposed an EEG ensemble learning framework (EEG-ELF) to predict motor recovery outcomes following rTMS therapy in stroke patients. The framework was evaluated on EEG recordings acquired during a motor imagery task from 20 individuals with subcortical stroke, collected both before and after a four-week rTMS intervention, along with the upper-limb Fugl-Meyer Assessment (FMA) scores. EEG-ELF's prediction performance was assessed through both regression and classification tasks using leave-one-subject-out cross-validation. The results demonstrated a strong correlation between predicted and actual recovery rates (Pearson's R = 0.92, p $< $ 0.001), and the framework achieved 95% accuracy in classifying patients into good and poor prognosis groups. These findings suggest that EEG-ELF can accurately predict individual motor outcomes from pre-treatment EEG features, highlighting its potential to guide personalized rTMS interventions through data-driven stratification of stroke patients.