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Related Concept Videos

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...

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Related Experiment Video

Updated: May 13, 2026

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
05:59

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Published on: October 6, 2023

Multiple circular-circular correlation coefficients for the quantification of phase synchronization processes in the

Katrin Pauen, Galina Ivanova

    Biomedizinische Technik. Biomedical Engineering
    |February 26, 2013
    PubMed
    Summary

    This study introduces new methods to measure phase synchronization, a key process for brain integration. These novel circular correlation coefficients help quantify complex phase couplings across brain areas.

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    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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    Published on: November 1, 2019

    Area of Science:

    • Neuroscience
    • Brain dynamics
    • Computational neuroscience

    Background:

    • Phase synchronization is a proposed mechanism for large-scale brain integration.
    • Quantifying synchrony is crucial for understanding brain function.
    • Cerebral integration involves multiple brain areas, necessitating multi-coupling analysis.

    Purpose of the Study:

    • To address the lack of a concept for multiple circular-circular correlation.
    • To present novel statistical methods for quantifying phase couplings.
    • To enable the analysis of phase synchronization between one dependent and multiple predictor signals.

    Main Methods:

    • Utilizing circular statistical methods due to the circular nature of phase values.
    • Developing and presenting two new multiple circular-circular correlation coefficients.
    • Applying these coefficients to quantify phase couplings in neural data.

    Main Results:

    • The proposed coefficients effectively quantify phase synchronization between multiple signals.
    • These methods provide a robust way to analyze complex phase couplings.
    • The study lays the groundwork for advanced analysis of brain integration.

    Conclusions:

    • The new multiple circular-circular correlation coefficients are valuable tools for neuroscience research.
    • These methods enhance our ability to study large-scale brain integration through phase synchronization.
    • Further research can utilize these coefficients to explore complex neural dynamics.