corrmap#

corrmap(STUDY, ALLEEG, n_tmp, index, *args, **kwargs)#

Find ICA maps correlated with one template component.

The calculation follows CORRMAP’s two-pass procedure: match every unique ICA decomposition to the requested component, build a polarity-aligned average map, then match again to that average. Automatic mode evaluates thresholds from 0.95 through 0.55 and chooses the most self-consistent pair of averages.

Parameters:

  • STUDY (dict[str, Any] | None) – EEGPrep STUDY dictionary.

  • ALLEEG (Any) – Loaded EEG datasets.

  • n_tmp (int) – One-based template dataset index in ALLEEG.

  • index (int) – One-based template ICA component.

  • *args (Any) – EEGLAB-style option/value pairs.

  • **kwargs (Any) – Options th, ics, clname, badcomps, resetclusters, chanlocs, title, pl, and plot.

Returns:

(CORRMAP, STUDY, ALLEEG) with correlations, selected components, polarity-aligned averages, and any requested cluster updates.

Return type:

tuple[dict[str, Any], dict[str, Any], list[dict[str, Any]]]

Notes

Component maps are RMS-normalized before averaging. Correlation is scale invariant, and the normalization prevents arbitrary ICA column scaling from weighting the second-pass template.

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