mne.viz.plot_ica_scores¶

mne.viz.plot_ica_scores(ica, scores, exclude=None, labels=None, axhline=None, title=’ICA component scores’, figsize=(12, 6), 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.

Parameters:

ica : instance of mne.preprocessing.ICA The ICA object. scores : array_like of float, shape (n ica components) | list of arrays Scores based on arbitrary metric to characterize ICA components. exclude : array_like of int The components marked for exclusion. If None (default), ICA.exclude will be used. labels : str | 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. axhline : float Draw horizontal line to e.g. visualize rejection threshold. title : str The figure title. figsize : tuple of int The figure size. Defaults to (12, 6). show : bool Show figure if True.

Returns:

fig : instance of matplotlib.pyplot.Figure The figure object