Data-driven estimation of mutual information using frequency domain and its application to epilepsy

We consider the problem of estimating mutual information between dependent data, an important problem in many science and engineering applications. We propose a data-driven estimator of mutual information in this paper. The main novelty of our solution lies in transforming the data to frequency domain to make the problem tractable. We define a novel metric-mutual information in frequency (Ml-in-frequency)-to detect and quantify the dependence between two random processes across frequency using Cramer's spectral representation. Our solution calculates mutual information as a function of frequency to estimate the mutual information between the dependent data over time and validate its performance on linear and nonlinear models. We then use our MI-in-frequency metric to infer the cross-frequency coupling during epileptic seizures, by analyzing electrocorticographic recordings from a total of eleven seizures in four medial temporal lobe epilepsy patients.