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Related Experiment Video

Updated: Oct 10, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Unravelling Causal Relationships Between Cortex and Muscle with Errors-in-variables Models.

Zhenghao Guo, Verity M McClelland, Zoran Cvetkovic

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 10, 2021
    PubMed
    Summary

    Errors-in-variables (EIV) models improve Granger causality (GC) estimation for electroencephalogram (EEG) and electromyogram (EMG) signals. This approach enhances detection of corticospinal pathways, even with noisy neurophysiological data.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Corticomuscular communication is vital for motor control.
    • Conventional Granger causality (GC) using linear regression (LR) struggles with noisy electroencephalogram (EEG) and electromyogram (EMG) signals.
    • Underestimation of causal links can occur in healthy individuals with good motor skills.

    Purpose of the Study:

    • To investigate Errors-in-Variables (EIV) models for improved GC estimation.
    • To assess the efficacy of EIV models in detecting linear time-invariant systems between EEG and EMG.
    • To compare EIV-based GC with conventional LR-based GC.

    Main Methods:

    • Application of Errors-in-Variables (EIV) models to Granger causality (GC) analysis.
    • Utilizing simulated data and real neurophysiological recordings (EEG and EMG).
    • Comparative analysis against standard linear regression (LR)-based GC methods.

    Main Results:

    • EIV models demonstrate superior performance in estimating corticospinal pathways compared to conventional GC.
    • The proposed EIV-based method effectively detects causal relationships in the presence of significant noise in both EEG and EMG signals.
    • Improved accuracy in identifying brain-muscle communication using noisy neurophysiological data.

    Conclusions:

    • Errors-in-Variables (EIV) models offer a significant advantage over traditional linear regression-based Granger causality (GC) for analyzing noisy EEG and EMG.
    • EIV-based GC is a more robust method for assessing corticospinal communication.
    • This advancement aids in understanding neural control of movement, particularly in challenging signal conditions.