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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Computational Biology

    Background:

    • Analyzing dynamic interactions in nonstationary neural systems is challenging.
    • Existing methods often lack precision in time and frequency domains.
    • Understanding oscillatory neocortical sensorimotor networks requires advanced connectivity analysis.

    Purpose of the Study:

    • To propose a novel parametric time-frequency conditional Granger causality (TF-CGC) method.
    • To enable high-precision connectivity analysis in multivariate, nonstationary systems.
    • To reveal dynamic interaction patterns in source electroencephalogram (EEG) signals.

    Main Methods:

    • Combined Geweke's spectral measure with time-varying autoregressive with exogenous input (TVARX) modeling.
    • Utilized a multiwavelet-based ultra-regularized orthogonal least squares (UROLS) algorithm with adjustable prediction error sum of squares (APRESS).
    • Employed regularization and ultra-least squares criteria for accurate time-varying model construction and causality tracking, eliminating indirect influences.

    Main Results:

    • Validated the TF-CGC method on simulations, demonstrating high time-frequency precision.
    • Applied to motor imagery (MI) EEGs, recovering predicted distributions accurately.
    • Identified physiologically interpretable connectivity patterns, providing new insights into cortical network organization.

    Conclusions:

    • The TF-CGC method effectively tracks rapidly varying causalities in EEG-based oscillatory networks.
    • The approach offers a powerful tool for analyzing complex neural dynamics.
    • Expected to yield significant information on neural mechanisms of perception and cognition.