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Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks

Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research...

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Detalles Bibliográficos
Autores principales: Lehnertz, Klaus, Rings, Thorsten, Bröhl, Timo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013076/
https://www.ncbi.nlm.nih.gov/pubmed/36925573
http://dx.doi.org/10.3389/fnetp.2021.755016
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author Lehnertz, Klaus
Rings, Thorsten
Bröhl, Timo
author_facet Lehnertz, Klaus
Rings, Thorsten
Bröhl, Timo
author_sort Lehnertz, Klaus
collection PubMed
description Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.
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spelling pubmed-100130762023-03-15 Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks Lehnertz, Klaus Rings, Thorsten Bröhl, Timo Front Netw Physiol Network Physiology Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks. Frontiers Media S.A. 2021-09-27 /pmc/articles/PMC10013076/ /pubmed/36925573 http://dx.doi.org/10.3389/fnetp.2021.755016 Text en Copyright © 2021 Lehnertz, Rings and Bröhl. https://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 Network Physiology
Lehnertz, Klaus
Rings, Thorsten
Bröhl, Timo
Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks
title Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks
title_full Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks
title_fullStr Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks
title_full_unstemmed Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks
title_short Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks
title_sort time in brain: how biological rhythms impact on eeg signals and on eeg-derived brain networks
topic Network Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013076/
https://www.ncbi.nlm.nih.gov/pubmed/36925573
http://dx.doi.org/10.3389/fnetp.2021.755016
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