mne.viz.plot_sparse_source_estimates#

mne.viz.plot_sparse_source_estimates(src, stcs, colors=None, linewidth=2, fontsize=18, bgcolor=(0.05, 0, 0.1), opacity=0.2, brain_color=(0.7, 0.7, 0.7), show=True, high_resolution=False, fig_name=None, fig_number=None, labels=None, modes=('cone', 'sphere'), scale_factors=(1, 0.6), verbose=None, **kwargs)[source]#

Plot source estimates obtained with sparse solver.

Active dipoles are represented in a “Glass” brain. If the same source is active in multiple source estimates it is displayed with a sphere otherwise with a cone in 3D.

Parameters
srcdict

The source space.

stcsinstance of SourceEstimate or list of instances of SourceEstimate

The source estimates.

colorslist

List of colors.

linewidthint

Line width in 2D plot.

fontsizeint

Font size.

bgcolortuple of length 3

Background color in 3D.

opacityfloat in [0, 1]

Opacity of brain mesh.

brain_colortuple of length 3

Brain color.

showbool

Show figures if True.

high_resolutionbool

If True, plot on the original (non-downsampled) cortical mesh.

fig_namestr

PyVista figure name.

fig_numberint

Matplotlib figure number.

labelsndarray or list of ndarray

Labels to show sources in clusters. Sources with the same label and the waveforms within each cluster are presented in the same color. labels should be a list of ndarrays when stcs is a list ie. one label for each stc.

modeslist

Should be a list, with each entry being 'cone' or 'sphere' to specify how the dipoles should be shown. The pivot for the glyphs in 'cone' mode is always the tail whereas the pivot in 'sphere' mode is the center.

scale_factorslist

List of floating point scale factors for the markers.

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.

**kwargskwargs

Keyword arguments to pass to renderer.mesh.

Returns
surfaceinstance of Figure3D

The 3D figure containing the triangular mesh surface.

Examples using mne.viz.plot_sparse_source_estimates#

Generate simulated evoked data

Generate simulated evoked data

Generate simulated evoked data
Source localization with a custom inverse solver

Source localization with a custom inverse solver

Source localization with a custom inverse solver
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior