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

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.Comment: This paper is accepted for publication in IEEE Transactions on Signal Processing and contains 15 pages, 9 figures and 1 tabl

Feature Clustering based MIM for a New Feature Extraction Method

In this paper, a new unsupervised Feature Extraction appoach is presented, which is based on feature clustering algorithm. Applying a divisive clustering algorithm, the method search for a compression of the information contained in the original set of features. It investigates the use of Mutual Information Maximization (MIM) to find appropriate transformation of clusterde features. Experiments on UCI datasets show that the proposed method often outperforms conventional unsupervised methods PCA and ICA from the point of view of classification accuracy