ICA analysis allows decomposing EEG/MEG data into independent components. The method behind ICA analysis is the extended
ICA algorithm (Lee TW et al. Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and
super-Gaussian sources. Neural Computation 11(2), 1999, 409-433). Before ICA is calculated, the dimensionality of data is
optionally reduced by PCA. By default, all PCA components are ignored that explain less than 1% variance. The use of PCA
can be switched off or the variance cutoff can be altered by pressing ICA / Options.