PMID- 33381007
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210101
IS  - 1662-4548 (Print)
IS  - 1662-453X (Electronic)
IS  - 1662-453X (Linking)
VI  - 14
DP  - 2020
TI  - Mutual Information of Multiple Rhythms for EEG Signals.
PG  - 574796
LID - 10.3389/fnins.2020.574796 [doi]
LID - 574796
AB  - 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.
CI  - Copyright (c) 2020 Ibanez-Molina, Soriano and Iglesias-Parro.
FAU - Ibanez-Molina, Antonio Jose
AU  - Ibanez-Molina AJ
AD  - Department of Psychology, University of Jaen, Jaen, Spain.
FAU - Soriano, Maria Felipa
AU  - Soriano MF
AD  - Unidad de salud mental, Hospital San Agustin, Linares, Spain.
FAU - Iglesias-Parro, Sergio
AU  - Iglesias-Parro S
AD  - Department of Psychology, University of Jaen, Jaen, Spain.
LA  - eng
PT  - Journal Article
DEP - 20201214
PL  - Switzerland
TA  - Front Neurosci
JT  - Frontiers in neuroscience
JID - 101478481
PMC - PMC7768085
OTO - NOTNLM
OT  - EEG rhythms
OT  - cross-frequency coupling
OT  - multiscale interactions
OT  - mutual information
OT  - neural oscillations
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2021/01/01 06:00
MHDA- 2021/01/01 06:01
PMCR- 2020/01/01
CRDT- 2020/12/31 05:24
PHST- 2020/06/21 00:00 [received]
PHST- 2020/11/20 00:00 [accepted]
PHST- 2020/12/31 05:24 [entrez]
PHST- 2021/01/01 06:00 [pubmed]
PHST- 2021/01/01 06:01 [medline]
PHST- 2020/01/01 00:00 [pmc-release]
AID - 10.3389/fnins.2020.574796 [doi]
PST - epublish
SO  - Front Neurosci. 2020 Dec 14;14:574796. doi: 10.3389/fnins.2020.574796. 
      eCollection 2020.