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arXiv · 1711.01629

Mutual Information in Frequency and its Application to Measure Cross-Frequency Coupling in Epilepsy

Rakesh Malladi·Don H Johnson·Giridhar P Kalamangalam·Nitin Tandon·Behnaam Aazhang
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Abstract

We define a metric, mutual information in frequency (MI-in-frequency), to detect and quantify the statistical dependence between different frequency components in the data, referred to as cross-frequency coupling and apply it to electrophysiological recordings from the brain to infer cross-frequency coupling. The current metrics used to quantify the cross-frequency coupling in neuroscience cannot detect if two frequency components in non-Gaussian brain recordings are statistically independent or not. Our MI-in-frequency metric, based on Shannon's mutual information between the Cramer's representation of stochastic processes, overcomes this shortcoming and can detect statistical dependence in frequency between non-Gaussian signals. We then describe two data-driven estimators of MI-in-frequency: one based on kernel density estimation and the other based on the nearest neighbor algorithm and validate their performance on simulated data. We then use MI-in-frequency to estimate mutual information between two data streams that are dependent across time, without making any parametric model assumptions. Finally, we use the MI-in- frequency metric to investigate the cross-frequency coupling in seizure onset zone from electrocorticographic recordings during seizures. The inferred cross-frequency coupling characteristics are essential to optimize the spatial and spectral parameters of electrical stimulation based treatments of epilepsy.

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Rakesh Malladi, Don H Johnson, Giridhar P Kalamangalam, Nitin Tandon, Behnaam Aazhang. 2018-03-15. Mutual Information in Frequency and its Application to Measure Cross-Frequency Coupling in Epilepsy. https://doi.org/10.1109/tsp.2018.2821627

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