PROJECT TITLE :

Mutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsy - 2018

ABSTRACT:

We tend to outline a metric, mutual info in frequency (MI-in-frequency), to detect and quantify the statistical dependence between completely different frequency components in the data, known as cross-frequency coupling and apply it to electrophysiological recordings from the brain to infer cross-frequency coupling. This metrics used to quantify the cross-frequency coupling in neuroscience cannot detect if two frequency parts in non-Gaussian brain recordings are statistically freelance or not. Our MI-in-frequency metric, primarily based on Shannon's mutual data between the Cramér's representation of stochastic processes, overcomes this shortcoming and can detect statistical dependence in frequency between non-Gaussian signals. We have a tendency to then describe two knowledge-driven estimators of MI-in-frequency: One primarily based on kernel density estimation and the other based on the closest neighbor algorithm and validate their performance on simulated knowledge. We have a tendency to then use MI-in-frequency to estimate mutual data between 2 data streams that are dependent across time, without making any parametric model assumptions. Finally, we have a tendency to 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 primarily based treatments of epilepsy.