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A Novel Method for Constructing EEG Large-Scale Cortical Dynamical Functional Network Connectivity (dFNC): WTCS.

Chanlin Yi, Ruwei Yao, Liuyi Song

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

    We developed a novel wavelet coherence-S estimator (WTCS) to analyze dynamic functional network connectivity (dFNC) using electroencephalogram (EEG) data. This method reveals brain network dynamics and aids in developing brain-inspired artificial neural networks.

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    Area of Science:

    • Neuroscience
    • Network Science
    • Signal Processing

    Background:

    • The brain's high-efficiency information processing relies on complex biological networks.
    • Large-scale dynamical functional network connectivity (dFNC) offers insights into brain activity but requires high temporal and spatial resolution.
    • Existing methods struggle to capture time-varying correlations in multivariate time series with differing spatial dimensions.

    Purpose of the Study:

    • To construct electroencephalogram (EEG)-based large-scale cortical dFNC for probing subtle brain dynamics.
    • To develop a novel method, the wavelet coherence-S estimator (WTCS), for assessing dynamic couplings among functional subnetworks.
    • To provide a new solution for analyzing time-varying couplings in multivariate time series.

    Main Methods:

    • Utilized electroencephalogram (EEG) data and brain atlas for cortical dFNC construction.
    • Developed and applied the wavelet coherence-S estimator (WTCS) to assess dynamic couplings.
    • Validated the method through simulation studies and application to real EEG data.

    Main Results:

    • The WTCS method demonstrated robustness and availability for dFNC analysis.
    • Analysis of real EEG data revealed distinct network properties like "Primary peak" and "P3-like peak".
    • Observed meaningful evolutions in dFNC network topology related to P300 responses.

    Conclusions:

    • The study provides new insights into dynamic and hierarchical brain activity analysis.
    • The developed WTCS method enhances dFNC studies and offers a novel approach for time-varying multivariate signal analysis.
    • Findings contribute to advancing brain-inspired artificial neural network development.