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Robust Granger Analysis in Lp Norm Space for Directed EEG Network Analysis.

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    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |June 11, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new Granger analysis (GA) method using Lp-norm to improve brain network construction from electroencephalogram (EEG) data. The Lp-GA effectively reduces artifact distortion, enhancing network reliability in noisy physiological recordings.

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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Computational Biology

    Background:

    • Granger analysis (GA) is crucial for directed brain network construction from physiological data like EEG.
    • Standard GA methods using L2 norm are sensitive to artifacts in real-world EEG, distorting network estimations.
    • Artifacts, such as ocular interference, can significantly impact the accuracy of directed link estimation.

    Purpose of the Study:

    • To develop an extended Granger analysis (GA) model robust to outliers and artifacts in physiological recordings.
    • To introduce an Lp-norm strategy within GA for improved causal connectivity estimation under noisy conditions.
    • To enhance the reliability of directed brain network construction from artifact-contaminated EEG data.

    Main Methods:

    • An extended Granger analysis (GA) model incorporating the Lp-norm was formulated to handle outlier conditions.
    • A feasible iteration procedure was employed to solve the novel Lp-norm GA model.
    • Quantitative evaluations were performed using simulated networks and real resting-state EEG data with ocular artifacts.

    Main Results:

    • The proposed Lp-norm Granger analysis (Lp-GA) demonstrated significantly smaller bias errors compared to L2-GA and Lasso-GA.
    • Lp-GA exhibited higher linkage consistency in network construction under various simulated outlier conditions.
    • Applications on EEG data showed Lp-GA effectively compressed ocular artifact influence, recovering reliable brain networks.

    Conclusions:

    • The Lp-norm strategy enhances Granger analysis (GA) robustness against physiological signal artifacts.
    • The proposed Lp-GA method offers a reliable approach for constructing directed brain networks from artifact-contaminated EEG.
    • This method is valuable for accurately capturing brain network structures in studies involving noisy physiological recordings.