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Mutual Information of Multiple Rhythms for EEG Signals

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 couplin...

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Autores principales: Ibáñez-Molina, Antonio José, Soriano, María Felipa, Iglesias-Parro, Sergio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768085/
https://www.ncbi.nlm.nih.gov/pubmed/33381007
http://dx.doi.org/10.3389/fnins.2020.574796
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author Ibáñez-Molina, Antonio José
Soriano, María Felipa
Iglesias-Parro, Sergio
author_facet Ibáñez-Molina, Antonio José
Soriano, María Felipa
Iglesias-Parro, Sergio
author_sort Ibáñez-Molina, Antonio José
collection PubMed
description 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.
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spelling pubmed-77680852020-12-29 Mutual Information of Multiple Rhythms for EEG Signals Ibáñez-Molina, Antonio José Soriano, María Felipa Iglesias-Parro, Sergio Front Neurosci Neuroscience 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. Frontiers Media S.A. 2020-12-14 /pmc/articles/PMC7768085/ /pubmed/33381007 http://dx.doi.org/10.3389/fnins.2020.574796 Text en Copyright © 2020 Ibáñez-Molina, Soriano and Iglesias-Parro. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Ibáñez-Molina, Antonio José
Soriano, María Felipa
Iglesias-Parro, Sergio
Mutual Information of Multiple Rhythms for EEG Signals
title Mutual Information of Multiple Rhythms for EEG Signals
title_full Mutual Information of Multiple Rhythms for EEG Signals
title_fullStr Mutual Information of Multiple Rhythms for EEG Signals
title_full_unstemmed Mutual Information of Multiple Rhythms for EEG Signals
title_short Mutual Information of Multiple Rhythms for EEG Signals
title_sort mutual information of multiple rhythms for eeg signals
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768085/
https://www.ncbi.nlm.nih.gov/pubmed/33381007
http://dx.doi.org/10.3389/fnins.2020.574796
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