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Cortical Source Analysis of High-Density EEG Recordings in Children
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Time-varying effective EEG source connectivity: the optimization of model parameters.

M Rubega, D Pascucci, J Rue Queralt

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary

    This study introduces a novel method to optimize adaptive Kalman filter parameters for analyzing brain network dynamics. Our approach ensures accurate neural signal estimation, improving the reliability of brain connectivity research.

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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Computational Biology

    Background:

    • Adaptive estimation methods, particularly the general Kalman filter, are crucial for studying dynamic brain networks due to the non-stationary nature of neural signals.
    • Accurate selection of model order (p) and adaptation constant (c) is vital for reliable time-varying multivariate autoregressive (TV-MVAR) estimates, preventing frequency domain biases and temporal distortions.

    Purpose of the Study:

    • To establish an objective criterion for optimally selecting the model order (p) and adaptation constant (c) in adaptive Kalman filter-based brain network analysis.
    • To address the limitations of residual- and information-based criteria that may not guarantee absolute minima for parameter selection.

    Main Methods:

    • Proposed a novel method utilizing partial derivatives of residual- and information-based criteria to guide the optimal selection of parameters p and c.
    • Validated the proposed method using human visual evoked potentials (VEPs) during face perception, a well-understood neural process.
    • Further validated the method with simulated data where the ground truth was known to assess performance rigorously.

    Main Results:

    • The developed objective criterion effectively guided the selection of optimal parameters (p and c) for adaptive Kalman filtering.
    • Demonstrated improved accuracy and reduced biases in time-varying multivariate autoregressive (TV-MVAR) estimates compared to sub-optimal filtering.
    • Validation with both real (human VEPs) and simulated data confirmed the method's efficacy in accurately capturing brain network dynamics.

    Conclusions:

    • The proposed partial derivative-based criterion offers a robust and objective approach for optimizing adaptive Kalman filter parameters in neuroscience.
    • Accurate parameter selection is essential for reliable brain network dynamic analysis, leading to more trustworthy interpretations of neural signal propagation and information processing.
    • This method enhances the precision of brain connectivity studies, contributing to a better understanding of neural processes like face perception.