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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Computational Biology

    Background:

    • Characterizing dynamic neural connectivity is crucial for understanding brain function.
    • Existing methods may struggle with multi-trial time-varying analyses.
    • Generalized Partial Directed Coherence (gPDC) offers a potential solution.

    Purpose of the Study:

    • To demonstrate the effectiveness of gPDC for time-varying neural connectivity analysis in a multi-trial setting.
    • To adapt single-trial gPDC statistical properties for multi-trial applications.
    • To validate the proposed time-varying gPDC method.

    Main Methods:

    • Extrapolation of single-trial asymptotic statistical results of gPDC to a multi-trial context.
    • Application of a sliding-window procedure for time-varying estimation.
    • Construction of time-frequency maps of channel connectivity.

    Main Results:

    • The study successfully illustrates the effectiveness of gPDC in characterizing time-varying neural connectivity.
    • The method was validated on a non-linear toy model with simulated EEG data.
    • Benchmarking was performed using a publicly available real EEG dataset.

    Conclusions:

    • The proposed gPDC-based approach effectively characterizes time-varying neural connectivity in multi-trial settings.
    • The sliding-window technique provides a robust method for analyzing dynamic brain networks.
    • This work contributes to advanced EEG data analysis techniques.