PROJECT TITLE :
Mutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsy - 2018
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.
Did you like this research project?
To get this research project Guidelines, Training and Code... Click Here