Functional Connectivity Associated with Severe Upper Limb Impairment in Resting-State Electroencephalography Among Chronic Stroke Survivors: A Machine Learning Approach

Background Severe upper limb impairment (ULI) presents a significant challenge in the rehabilitation of chronic stroke survivors, affecting their quality of life. Identifying biomarkers and understanding the neural mechanisms associated with severe ULI are essential for evaluating recovery potential and enhancing rehabilitation effectiveness. This study aimed to identify resting-state electroencephalography (EEG) functional connectivity features related to severe ULI in chronic stroke survivors using machine learning (ML) methods. Methods EEG data were collected from 34 chronic stroke survivors. Participants were categorized into two labels based on their Fugl-Meyer assessment for upper extremity (FMA-UE) scores: a mild/moderate ULI (FMA-UE ≥ 30; n = 19) and a severe ULI (FMA-UE < 30; n = 15). We employed ML algorithms to classify severe ULI, including logistic regression with L1, elastic net regularization, stochastic gradient descent, and support vector machines, along with several feature selection methods. Coherence was evaluated across six frequency bands within both the ipsilesional (affected by the lesion) and contralesional (opposite side of the lesion) hemispheres. Results The logistic regression model with L1 and ReliefF feature selection methods was the most effective, achieving a balanced accuracy of 0.91 (sensitivity = 0.93, specificity = 0.90). This approach identified 14 significant features for distinguishing severe ULI from mild to moderate ULI, including delta interhemispheric and intrahemispheric connectivity of the frontal, parietal, and temporal regions. Additionally, interhemispheric and intrahemispheric theta connectivity was observed in the prefrontal, frontal, temporal, and parietal regions. Low-beta intrahemispheric connectivity was also observed in the contralesional parietal regions. Conclusions Our research highlights the association between alterations in connectivity within low-frequency bands and severe ULI across widespread brain regions, including areas outside the sensorimotor cortex and bilateral intrahemispheric and interhemispheric regions. Further research utilizing larger longitudinal datasets from early stroke survivors employing ML approaches could contribute to the development of more accurate predictive models for motor recovery and rehabilitation responses.