Functional connectivity associated with severe upper limb impairment in resting-state electroencephalography among chronic stroke survivors: a machine learning approach.

ABSTRACT:

Background

Severe upper limb impairment (ULI) presents a significant challenge in the rehabilitation of chronic stroke survivors and affects 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 associated with 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 groups based on their Fugl-Meyer Assessment for Upper Extremity (FMA-UE) scores: a mild/moderate ULI (FMA-UE ≥ 30; n = 19) and severe ULI

SUBMITTER: Lee JY 

PROVIDER: S-EPMC12751579 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature