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Related Experiment Video

Updated: Dec 26, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Estimating Fugl-Meyer Upper Extremity Motor Score From Functional-Connectivity Measures.

Nader Riahi, Vasily A Vakorin, Carlo Menon

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |March 10, 2020
    PubMed
    Summary

    Resting-state electroencephalography (EEG) functional connectivity shows promise as a biomarker for estimating Fugl-Meyer upper extremity motor scores in stroke survivors, potentially overcoming challenges with trained examiner availability.

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    Area of Science:

    • Neuroscience
    • Biomarkers
    • Rehabilitation Medicine

    Background:

    • The Fugl-Meyer assessment (FMA) is a standard for evaluating motor function post-stroke.
    • Limited availability of trained examiners poses a challenge for widespread FMA implementation.
    • Neurophysiological biomarkers offer a potential solution to this accessibility issue.

    Purpose of the Study:

    • To investigate resting-state electroencephalographic (EEG) functional connectivity as a biomarker for estimating the Fugl-Meyer upper extremity motor score (FMU) in chronic stroke.
    • To identify specific EEG connectivity patterns and frequencies that correlate with motor function.
    • To develop a model for predicting FMU using EEG biomarkers.

    Main Methods:

    • Resting-state EEG data were collected from 10 individuals with chronic stroke.
    • Functional connectivity was assessed using five algorithms, quantifying maximum coherence across 15 frequencies (1-45 Hz).
    • Partial Least Squares (PLS) Correlation and Regression analyses were employed to identify predictive EEG features and estimate FMU.

    Main Results:

    • A cross-validation on the training set (n=8) using the Phase-Lag-Index algorithm yielded R²=0.97 and a root-mean-square error of 1.9.
    • The predictive model accurately estimated FMU in an independent test set (n=2), with predicted scores closely matching actual scores.
    • Specific EEG electrode pairings and frequencies were identified as robust correlates of FMU.

    Conclusions:

    • Resting-state EEG functional connectivity measures can serve as reliable biomarkers for estimating upper extremity motor function in chronic stroke.
    • This approach shows potential to overcome the limitations associated with the need for trained examiners in traditional motor assessments.
    • The findings support the development of objective, neurophysiologically-based tools for stroke motor rehabilitation assessment.