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

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.