mne.viz.plot_ica_properties

mne.viz.plot_ica_properties(ica, inst, picks=None, axes=None, dB=True, plot_std=True, log_scale=False, topomap_args=None, image_args=None, psd_args=None, figsize=None, show=True, reject='auto', reject_by_annotation=True, *, estimate='power', verbose=None)

Display component properties.

Properties include the topography, epochs image, ERP/ERF, power spectrum, and epoch variance.

Parameters:

icainstance of mne.preprocessing.ICA

The ICA solution.

instinstance of Epochs or Raw

The data to use in plotting properties.

Note

You can interactively cycle through topographic maps for different channel types by pressing T.

picksint | list of int | slice | None

Indices of the independent components (ICs) to visualize. If an integer, represents the index of the IC to pick. Multiple ICs can be selected using a list of int or a slice. The indices are 0-indexed, so picks=1 will pick the second IC: ICA001. None will pick the first 5 components.

axeslist of Axes | None

List of five matplotlib axes to use in plotting: [topomap_axis, image_axis, erp_axis, spectrum_axis, variance_axis]. If None a new figure with relevant axes is created. Defaults to None.

dBbool

Whether to plot spectrum in dB. Defaults to True.

plot_stdbool | float

Whether to plot standard deviation/confidence intervals in ERP/ERF and spectrum plots. Defaults to True, which plots one standard deviation above/below for the spectrum. If set to float allows to control how many standard deviations are plotted for the spectrum. For example 2.5 will plot 2.5 standard deviation above/below. For the ERP/ERF, by default, plot the 95 percent parametric confidence interval is calculated. To change this, use ci in ts_args in image_args (see below).

log_scalebool

Whether to use a logarithmic frequency axis to plot the spectrum. Defaults to False.

Note

You can interactively toggle this setting by pressing L.

New in v1.1.

topomap_argsdict | None

Dictionary of arguments to plot_topomap. If None, doesn’t pass any additional arguments. Defaults to None.

image_argsdict | None

Dictionary of arguments to plot_epochs_image. If None, doesn’t pass any additional arguments. Defaults to None.

psd_argsdict | None

Dictionary of arguments to compute_psd(). If None, doesn’t pass any additional arguments. Defaults to None.

figsizearray_like, shape (2,) | None

Allows to control size of the figure. If None, the figure size defaults to [7., 6.].

showbool

Show figure if True.

reject‘auto’ | dict | None

Allows to specify rejection parameters used to drop epochs (or segments if continuous signal is passed as inst). If None, no rejection is applied. The default is ‘auto’, which applies the rejection parameters used when fitting the ICA object.

reject_by_annotationbool

Whether to omit bad segments from the data before fitting. If True (default), annotated segments whose description begins with 'bad' are omitted. If False, no rejection based on annotations is performed.

Has no effect if inst is not a mne.io.Raw object.

New in v0.21.0.

estimatestr, {‘power’, ‘amplitude’}

Can be “power” for power spectral density (PSD; default), “amplitude” for amplitude spectrum density (ASD).

New in v1.8.0.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

figlist

List of matplotlib figures.

Notes

New in v0.13.