mne.viz.plot_ica_scores(ica, scores, exclude=None, labels=None, axhline=None, title='ICA component scores', figsize=None, n_cols=None, show=True)[source]#

Plot scores related to detected components.

Use this function to asses how well your score describes outlier sources and how well you were detecting them.

icainstance of mne.preprocessing.ICA

The ICA object.

scoresarray_like of float, shape (n_ica_components,) | list of array

Scores based on arbitrary metric to characterize ICA components.

excludearray_like of int

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

labelsstr | list | ‘ecg’ | ‘eog’ | None

The labels to consider for the axes tests. Defaults to None. If list, should match the outer shape of scores. If ‘ecg’ or ‘eog’, the labels_ attributes will be looked up. Note that ‘/’ is used internally for sublabels specifying ECG and EOG channels.


Draw horizontal line to e.g. visualize rejection threshold.


The figure title.

figsizetuple of int | None

The figure size. If None it gets set automatically.

n_colsint | None

Scores are plotted in a grid. This parameter controls how many to plot side by side before starting a new row. By default, a number will be chosen to make the grid as square as possible.


Show figure if True.

figinstance of Figure

The figure object.