class eelbrain.pipeline.RawICA(source, session, method='extended-infomax', random_state=0, cache=False, **kwargs)

ICA raw pipe

  • source (str) – Name of the raw pipe to use for input data.

  • session (str | sequence of str) – Session(s) to use for estimating ICA components.

  • method (str) – Method for ICA decomposition (default: 'extended-infomax'; see mne.preprocessing.ICA).

  • random_state (int) – Set the random state for ICA decomposition to make results reproducible (default 0, see mne.preprocessing.ICA).

  • cache (bool) – Cache the resulting raw files (default False).

  • ... – Additional parameters for mne.preprocessing.ICA.


This preprocessing step estimates one set of ICA components per subject, using the data specified in the session parameter. The selected components are then removed from all data sessions during this preprocessing step, regardless of whether they were used to estimate the components or not.

Use make_ica_selection() for each subject to select ICA components that should be removed. The arguments to that function determine what data is used to visualize the component time courses. For example, to determine which components load strongly on empty room data, use e.make_ica_selection(session='emptyroom') (assuming an 'emptyroom' session is present).

This step merges bad channels from all sessions.