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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations ove...

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Detalles Bibliográficos
Autores principales: Heisz, Jennifer J., McIntosh, Anthony R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MyJove Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3729183/
https://www.ncbi.nlm.nih.gov/pubmed/23851571
http://dx.doi.org/10.3791/50131
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author Heisz, Jennifer J.
McIntosh, Anthony R.
author_facet Heisz, Jennifer J.
McIntosh, Anthony R.
author_sort Heisz, Jennifer J.
collection PubMed
description When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations over time as "noise". However, it is becoming clear that brain signal variability conveys meaningful functional information about neural network dynamics. This article describes the novel method of multiscale entropy (MSE) for quantifying brain signal variability. MSE may be particularly informative of neural network dynamics because it shows timescale dependence and sensitivity to linear and nonlinear dynamics in the data.
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spelling pubmed-37291832013-08-07 Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy Heisz, Jennifer J. McIntosh, Anthony R. J Vis Exp Neuroscience When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations over time as "noise". However, it is becoming clear that brain signal variability conveys meaningful functional information about neural network dynamics. This article describes the novel method of multiscale entropy (MSE) for quantifying brain signal variability. MSE may be particularly informative of neural network dynamics because it shows timescale dependence and sensitivity to linear and nonlinear dynamics in the data. MyJove Corporation 2013-06-27 /pmc/articles/PMC3729183/ /pubmed/23851571 http://dx.doi.org/10.3791/50131 Text en Copyright © 2013, Journal of Visualized Experiments http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visithttp://creativecommons.org/licenses/by-nc-nd/3.0/
spellingShingle Neuroscience
Heisz, Jennifer J.
McIntosh, Anthony R.
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
title Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
title_full Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
title_fullStr Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
title_full_unstemmed Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
title_short Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
title_sort applications of eeg neuroimaging data: event-related potentials, spectral power, and multiscale entropy
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3729183/
https://www.ncbi.nlm.nih.gov/pubmed/23851571
http://dx.doi.org/10.3791/50131
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