Mutual Information of Multiple Rhythms for EEG Signals
| Autor: | María Felipa Soriano, Sergio Iglesias-Parro, Antonio J. Ibáñez-Molina |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Computer science neural oscillations multiscale interactions cross-frequency coupling Electroencephalography Information theory Signal lcsh:RC321-571 Background noise 03 medical and health sciences 0302 clinical medicine Rhythm medicine mutual information lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Original Research EEG rhythms medicine.diagnostic_test business.industry General Neuroscience Pattern recognition Mutual information 030104 developmental biology Neural oscillation A priori and a posteriori Artificial intelligence business 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | Frontiers in Neuroscience, Vol 14 (2020) Frontiers in Neuroscience |
| Popis: | 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 withouta prioriassumptions. |
| Databáze: | OpenAIRE |
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