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

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dc.citation.firstpage3008en_US
dc.citation.issueNumber11en_US
dc.citation.journalTitleIEEE Transactions on Signal Processingen_US
dc.citation.lastpage3023en_US
dc.citation.volumeNumber66en_US
dc.contributor.authorMalladi, Rakeshen_US
dc.contributor.authorJohnson, Don H.en_US
dc.contributor.authorKalamangalam, Giridhar P.en_US
dc.contributor.authorTandon, Nitinen_US
dc.contributor.authorAazhang, Behnaamen_US
dc.date.accessioned2018-08-21T16:18:43Zen_US
dc.date.available2018-08-21T16:18:43Zen_US
dc.date.issued2018en_US
dc.description.abstractWe 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.en_US
dc.identifier.citationMalladi, 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.en_US
dc.identifier.digital08329533en_US
dc.identifier.doihttps://doi.org/10.1109/TSP.2018.2821627en_US
dc.identifier.urihttps://hdl.handle.net/1911/102479en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsArticle 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.en_US
dc.titleMutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsyen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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