Mutual Information of Multiple Rhythms for EEG Signals

Frontiers in Neuroscience

Sergio Iglesias-Parro¹  Antonio José Ibáñez-Molina¹  María Felipa Soriano² 

[1] Department of Psychology, University of Jaén, Jaén, Spain;Unidad de salud mental, Hospital San Agustín, Linares, Spain;

关键词: cross-frequency coupling;    mutual information;    EEG rhythms;    multiscale interactions;    neural oscillations;   

DOI  :  10.3389/fnins.2020.574796

来源: DOAJ

【 摘 要 】

Electroencephalograms (EEG) are one of the most commonly used measures to study brain functioning at a macroscopic level. The structure of the EEG time series is composed of many neural rhythms interacting at different spatiotemporal scales. This interaction is often named as cross frequency coupling, and consists of transient couplings between various parameters of different rhythms. This coupling has been hypothesized to be a basic mechanism involved in cognitive functions. There are several methods to measure cross frequency coupling between two rhythms but no single method has been selected as the gold standard. Current methods only serve to explore two rhythms at a time, are computationally demanding, and impose assumptions about the nature of the signal. Here we present a new approach based on Information Theory in which we can characterize the interaction of more than two rhythms in a given EEG time series. It estimates the mutual information of multiple rhythms (MIMR) extracted from the original signal. We tested this measure using simulated and real empirical data. We simulated signals composed of three frequencies and background noise. When the coupling between each frequency component was manipulated, we found a significant variation in the MIMR. In addition, we found that MIMR was sensitive to real EEG time series collected with open vs. closed eyes, and intra-cortical recordings from epileptic and non-epileptic signals registered at different regions of the brain. MIMR is presented as a tool to explore multiple rhythms, easy to compute and without a priori assumptions.

【 授权许可】

Unknown