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Clin Neurophysiol. 2008 Jun;119(6):1262-70. doi: 10.1016/j.clinph.2007.12.023.

Establishing correlations of scalp field maps with other experimental variables using covariance analysis and resampling methods.

Author information

  • 1Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, Bolligenstr. 111, 3000 Bern 60, Switzerland. thomas.koenig@puk.unibe.ch

Abstract

OBJECTIVE:

In EEG/MEG experiments, increasing the number of sensors improves the spatial resolution of the results. However, the standard statistical methods are inappropriate for these multivariate, highly correlated datasets. We introduce a procedure to identify spatially extended scalp fields that correlate with some external, continuous measure (reaction-time, performance, clinical status) and to test their significance.

METHODS:

We formally deduce that the channel-wise covariance of some experimental variable with scalp field data directly represents intracerebral sources associated with that variable. We furthermore show how the significance of such a representation can be tested with resampling techniques.

RESULTS:

Simulations showed that depending on the number of channels and subjects, effects can be detected already at low signal to noise ratios. In a sample analysis of real data, we found that foreign-language evoked ERP data were significantly associated with foreign-language proficiency. Inverse solutions of the extracted covariances pointed to sources in language-related areas.

CONCLUSIONS:

Covariance mapping combined with bootstrapping methods has high statistical power and yields unique and directly interpretable results.

SIGNIFICANCE:

The introduced methodology overcomes some of the 'traditional' statistical problems in EEG/MEG scalp data analysis. Its application can improve the reproducibility of results in the field of EEG/MEG.

PMID:
18424230
[PubMed - indexed for MEDLINE]
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