title: Accounting for linear transformations of EEG and MEG data in source analysis.
abstract: 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.
authors.affiliation: Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany; MEG-Center, University of Tübingen, Tübingen, Germany.
authors.forename: Joerg F
authors.initials: JF
authors.lastname: Hipp
authors.affiliation: Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany; MEG-Center, University of Tübingen, Tübingen, Germany.
authors.forename: Markus
authors.initials: M
authors.lastname: Siegel
mesh_terms: D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping; D004569:Electroencephalography / Q000706:statistics & numerical data*; D006801:Humans; D016247:Information Storage and Retrieval; D016014:Linear Models; D015225:Magnetoencephalography / Q000706:statistics & numerical data*; D008959:Models, Neurological; D010364:Pattern Recognition, Visual / Q000502:physiology; D013647:Task Performance and Analysis
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