Malladi, RakeshJohnson, Don H.Kalamangalam, Giridhar P.Tandon, NitinAazhang, Behnaam2018-08-212018-08-212018Malladi, Rakesh, Johnson, Don H., Kalamangalam, Giridhar P., et al.. "Mutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsy." <i>IEEE Transactions on Signal Processing,</i> 66, no. 11 (2018) IEEE: 3008-3023. https://doi.org/10.1109/TSP.2018.2821627.https://hdl.handle.net/1911/102479We 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 Cramér'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.engArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.Journal article08329533https://doi.org/10.1109/TSP.2018.2821627