mne.viz.plot_ica_overlay

mne.viz.plot_ica_overlay(ica, inst, exclude=None, picks=None, start=None, stop=None, title=None, show=True, n_pca_components=None)

Overlay of raw and cleaned signals given the unmixing matrix.

This method helps visualizing signal quality and artifact rejection.

Parameters

icainstance of mne.preprocessing.ICA

The ICA object.

instinstance of mne.io.Raw or mne.Evoked

The signals to be compared given the ICA solution. If Raw input, The raw data are displayed before and after cleaning. In a second panel the cross channel average will be displayed. Since dipolar sources will be canceled out this display is sensitive to artifacts. If evoked input, butterfly plots for clean and raw signals will be superimposed.

excludearray_like of int | None (default)

The components marked for exclusion. If None (default), ICA.exclude will be used.

picksstr | list | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels that were included during fitting.

startint

X-axis start index. If None from the beginning.

stopint

X-axis stop index. If None to the end.

titlestr

The figure title.

showbool

Show figure if True.

n_pca_componentsint | float | None

The number of PCA components to be kept, either absolute (int) or fraction of the explained variance (float). If None (default), the ica.n_pca_components from initialization will be used in 0.22; in 0.23 all components will be used.

New in version 0.22.

Returns

figinstance of Figure

The figure.

Examples using mne.viz.plot_ica_overlay