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.
dict
The source space.
SourceEstimate
or list
of instances of SourceEstimate
The source estimates.
list
List of colors.
int
Line width in 2D plot.
int
Font size.
tuple
of length 3Background color in 3D.
float
in [0, 1]Opacity of brain mesh.
tuple
of length 3Brain color.
Show figures if True.
If True, plot on the original (non-downsampled) cortical mesh.
str
PyVista figure name.
int
Matplotlib figure number.
ndarray
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.
list
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.
list
List of floating point scale factors for the markers.
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.
Keyword arguments to pass to renderer.mesh.
Figure3D
The 3D figure containing the triangular mesh surface.
mne.viz.plot_sparse_source_estimates
#Generate simulated evoked data
Source localization with a custom inverse solver
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
Compute MxNE with time-frequency sparse prior