eelbrain.pipeline.RawICA

class eelbrain.pipeline.RawICA(source, session, method='extended-infomax', random_state=0, fit_kwargs=None, cache=False, **kwargs)

ICA raw pipe

Parameters:

  • source (eelbrain._experiment.preprocessing.RawPipe) – Name of the raw pipe to use for input data.

  • session (str | Sequence[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).

  • fit_kwargs (Dict[str, Any]) – A dictionary with keyword arguments that should be passed to mne.preprocessing.ICA.fit(). This includes reject={'mag': 5e-12, 'grad': 5000e-13, 'eeg': 300e-6} unless a different value for reject is specified here.

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

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

See also

MneExperiment.raw

Notes

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.