Multiple linear regression to estimate time-frequency electrophysiological responses in single trials

Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in...

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Main Authors:	Hu, L., Zhang, Z.G., Mouraux, A., Iannetti, G.D.
Format:	Online
Language:	English
Published:	Academic Press 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401443/

id	pubmed-4401443
recordtype	oai_dc
spelling	pubmed-44014432015-05-01 Multiple linear regression to estimate time-frequency electrophysiological responses in single trials Hu, L. Zhang, Z.G. Mouraux, A. Iannetti, G.D. Article Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical oscillations, obtaining single-trial estimate of response latency, frequency, and magnitude. This permits within-subject statistical comparisons, correlation with pre-stimulus features, and integration of simultaneously-recorded EEG and fMRI. Academic Press 2015-05-01 /pmc/articles/PMC4401443/ /pubmed/25665966 http://dx.doi.org/10.1016/j.neuroimage.2015.01.062 Text en © 2015 The Authors. Published by Elsevier Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Hu, L. Zhang, Z.G. Mouraux, A. Iannetti, G.D.
spellingShingle	Hu, L. Zhang, Z.G. Mouraux, A. Iannetti, G.D. Multiple linear regression to estimate time-frequency electrophysiological responses in single trials
author_facet	Hu, L. Zhang, Z.G. Mouraux, A. Iannetti, G.D.
author_sort	Hu, L.
title	Multiple linear regression to estimate time-frequency electrophysiological responses in single trials
title_short	Multiple linear regression to estimate time-frequency electrophysiological responses in single trials
title_full	Multiple linear regression to estimate time-frequency electrophysiological responses in single trials
title_fullStr	Multiple linear regression to estimate time-frequency electrophysiological responses in single trials
title_full_unstemmed	Multiple linear regression to estimate time-frequency electrophysiological responses in single trials
title_sort	multiple linear regression to estimate time-frequency electrophysiological responses in single trials
description	Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical oscillations, obtaining single-trial estimate of response latency, frequency, and magnitude. This permits within-subject statistical comparisons, correlation with pre-stimulus features, and integration of simultaneously-recorded EEG and fMRI.
publisher	Academic Press
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401443/
_version_	1613212926857969664

Multiple linear regression to estimate time-frequency electrophysiological responses in single trials

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