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Related Concept Videos

Excitation-Contraction Coupling in Skeletal Muscles01:20

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
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Related Experiment Video

Updated: Oct 24, 2025

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
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Multiscale Wavelet Transfer Entropy With Application to Corticomuscular Coupling Analysis.

Zhenghao Guo, Verity M McClelland, Osvaldo Simeone

    IEEE Transactions on Bio-Medical Engineering
    |August 16, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method using multiscale wavelet transfer entropy to better measure brain-muscle communication. It detects both linear and non-linear interactions, improving upon older methods for analyzing sensorimotor neurophysiology.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Cortico-muscular coupling (CMC) is vital for motor control, typically assessed using linear methods like cortico-muscular coherence (CMC) and Granger causality (GC).
    • Existing methods lack sensitivity and fail to capture non-linear interactions or identify coupling in individuals with good motor skills but no significant linear CMC/GC.
    • A need exists for advanced analytical tools to comprehensively understand brain-muscle communication.

    Purpose of the Study:

    • To develop novel measures of functional cortico-muscular coupling with enhanced sensitivity.
    • To create methodologies capable of detecting both linear and non-linear interactions between the motor cortex and muscle activity.
    • To improve the analysis of information flow between electroencephalogram (EEG) and electromyogram (EMG) signals.

    Main Methods:

    • A multiscale wavelet transfer entropy (TE) methodology was developed.
    • Dyadic stationary wavelet transform was used to decompose EEG and EMG signals into neural oscillation bands.
    • Transfer entropy analysis was applied across various embedding delay vectors to quantify coupling at different time scales and frequencies.

    Main Results:

    • The proposed methodology successfully detected and quantified information flow between EEG and EMG signals in subjects, including those lacking significant linear CMC or GC.
    • Non-linear cross-frequency interactions and interactions across different temporal scales were identified.
    • Results align with established sensorimotor neurophysiology, validating the approach.

    Conclusions:

    • Multiscale wavelet transfer entropy offers a comprehensive framework for analyzing cortex-muscle interactions.
    • The developed methodologies provide deeper insights into movement control and broader neurophysiological processes.
    • This approach enhances the understanding of brain-body communication beyond traditional linear analyses.