Analysis of EEG/MEG map topographies and source distributions on the epoch level using non-parametric randomization tests
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1
Compumedics, Neuroscan, Germany
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2
Compumedics, Neuroscan, United States
In Event-Related Potential (ERP) and Event-Related Field (ERF) experiments, stimuli are presented repeatedly, and the subject's brain response is recorded using EEG or MEG, respectively. After artifact removal, epoching, and averaging, though, it is no longer possible to establish whether and for which latencies the averaged waveforms are significantly different between stimulus types, nor whether the epochs per stimulus type are consistent enough to warrant averaging them in the first place. A statistical analysis across epochs can provide exactly this information.
Traditional statistical measures in channel space such as the t-test make disputable assumptions regarding repeatability and independence. Therefore, non-parametric methods have recently attracted attention for the analysis of ERPs and ERFs.
In this contribution, a framework is proposed that allows the application of non-parametric methods such as Topographic Analysis of Variance (TANOVA) and Statistical non-Parametric Mapping of Current Density Reconstructions (CDR SnPM) not only to individual averages in the context of a group study but to the individual epochs themselves, even for single-subject data. Unlike described in previous publications, the statistical analysis is conducted sample-by-sample as opposed to using a maximum statistic over all samples. The then necessary multiple comparison correction is based on the spectral properties of the data. For CDR SnPM, in addition to a test for significant differences between conditions, a within-condition consistency test is used to justify testing for differences on a sample-by-sample basis.
A visual Continuous Performance Task (CPT) EEG experiment eliciting Mismatch Negativity (MMN) is used to demonstrate the methods. Latencies and brain locations where the brain response differs significantly between stimulus types are consistent with what is known about the MMN.
Keywords:
Electroencephalography,
Magnetoencephalography,
Event-related potentials,
statistical analysis,
Event-Related Fields,
Continuous Performance Task,
Current Density Analysis,
Non-Parametrical Statistics,
Randomization Statistics,
Statistical non-Parametric Mapping,
Topographical Analysis of Variance
Conference:
XII International Conference on Cognitive Neuroscience (ICON-XII), Brisbane, Queensland, Australia, 27 Jul - 31 Jul, 2014.
Presentation Type:
Oral Presentation
Topic:
Methods Development
Citation:
Wagner
M,
Ponton
C,
Tech
R,
Fuchs
M and
Kastner
J
(2015). Analysis of EEG/MEG map topographies and source distributions on the epoch level using non-parametric randomization tests.
Conference Abstract:
XII International Conference on Cognitive Neuroscience (ICON-XII).
doi: 10.3389/conf.fnhum.2015.217.00060
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Received:
19 Feb 2015;
Published Online:
24 Apr 2015.
*
Correspondence:
Dr. Michael Wagner, Compumedics, Neuroscan, Hamburg, Germany, mwagner@neuroscan.com