EEG ensemble learning framework for prognostic prediction of post-rTMS motor recovery in stroke
File(s)
Accepted version
Author(s)
Type
Journal Article
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.
(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.
Date Issued
2026-05-27
Date Acceptance
2026-05-01
Citation
IEEE Transactions on Biomedical Engineering, 2026
ISSN
0018-9294
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Journal / Book Title
IEEE Transactions on Biomedical Engineering
Copyright Statement
Copyright © 2026 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published online
Date Publish Online
2026-05-27