Accounting for linear transformations of EEG and MEG data in source analysis.

Analyses of electro- and magnetoencephalography (EEG, MEG) data often involve a linear modification of signals at the sensor level. Examples include re-referencing of the EEG, computation of synthetic gradiometer in MEG, or the removal of artifactual independent components to clean EEG and MEG data....

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Main Authors: Joerg F Hipp, Markus Siegel
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0121048
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author Joerg F Hipp
Markus Siegel
author_facet Joerg F Hipp
Markus Siegel
author_sort Joerg F Hipp
collection DOAJ
description Analyses of electro- and magnetoencephalography (EEG, MEG) data often involve a linear modification of signals at the sensor level. Examples include re-referencing of the EEG, computation of synthetic gradiometer in MEG, or the removal of artifactual independent components to clean EEG and MEG data. A question of practical relevance is, if such modifications must be accounted for by adapting the physical forward model (leadfield) before subsequent source analysis. Here, we show that two scenarios need to be differentiated. In the first scenario, which corresponds to re-referencing the EEG and synthetic gradiometer computation in MEG, the leadfield must be adapted before source analysis. In the second scenario, which corresponds to removing artifactual components to 'clean' the data, the leadfield must not be changed. We demonstrate and discuss the consequences of wrongly modifying the leadfield in the latter case for an example. Future EEG and MEG studies employing source analyses should carefully consider whether and, if so, how the leadfield must be modified as explicated here.
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spelling doaj.art-8fa9b22b908541bbb628c12496b37b772022-12-21T19:11:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01104e012104810.1371/journal.pone.0121048Accounting for linear transformations of EEG and MEG data in source analysis.Joerg F HippMarkus SiegelAnalyses of electro- and magnetoencephalography (EEG, MEG) data often involve a linear modification of signals at the sensor level. Examples include re-referencing of the EEG, computation of synthetic gradiometer in MEG, or the removal of artifactual independent components to clean EEG and MEG data. A question of practical relevance is, if such modifications must be accounted for by adapting the physical forward model (leadfield) before subsequent source analysis. Here, we show that two scenarios need to be differentiated. In the first scenario, which corresponds to re-referencing the EEG and synthetic gradiometer computation in MEG, the leadfield must be adapted before source analysis. In the second scenario, which corresponds to removing artifactual components to 'clean' the data, the leadfield must not be changed. We demonstrate and discuss the consequences of wrongly modifying the leadfield in the latter case for an example. Future EEG and MEG studies employing source analyses should carefully consider whether and, if so, how the leadfield must be modified as explicated here.https://doi.org/10.1371/journal.pone.0121048
spellingShingle Joerg F Hipp
Markus Siegel
Accounting for linear transformations of EEG and MEG data in source analysis.
PLoS ONE
title Accounting for linear transformations of EEG and MEG data in source analysis.
title_full Accounting for linear transformations of EEG and MEG data in source analysis.
title_fullStr Accounting for linear transformations of EEG and MEG data in source analysis.
title_full_unstemmed Accounting for linear transformations of EEG and MEG data in source analysis.
title_short Accounting for linear transformations of EEG and MEG data in source analysis.
title_sort accounting for linear transformations of eeg and meg data in source analysis
url https://doi.org/10.1371/journal.pone.0121048
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