mne.viz.Brain#

class mne.viz.Brain(subject_id, hemi='both', surf='pial', title=None, cortex='classic', alpha=1.0, size=800, background='black', foreground=None, figure=None, subjects_dir=None, views='auto', offset='auto', show_toolbar=False, offscreen=False, interaction='trackball', units='mm', view_layout='vertical', silhouette=False, theme=None, show=True, block=False)[source]#

Class for visualizing a brain.

Warning

The API for this class is not currently complete. We suggest using mne.viz.plot_source_estimates() with the PyVista backend enabled to obtain a Brain instance.

Parameters
subject_idstr

Subject name in Freesurfer subjects dir.

hemistr

Hemisphere id (ie ‘lh’, ‘rh’, ‘both’, or ‘split’). In the case of ‘both’, both hemispheres are shown in the same window. In the case of ‘split’ hemispheres are displayed side-by-side in different viewing panes.

surfstr

FreeSurfer surface mesh name (ie ‘white’, ‘inflated’, etc.).

titlestr

Title for the window.

cortexstr, list, dict

Specifies how the cortical surface is rendered. Options:

  1. The name of one of the preset cortex styles:

    'classic' (default), 'high_contrast', 'low_contrast', or 'bone'.

  2. A single color-like argument to render the cortex as a single

    color, e.g. 'red' or (0.1, 0.4, 1.).

  3. A list of two color-like used to render binarized curvature

    values for gyral (first) and sulcal (second). regions, e.g., ['red', 'blue'] or [(1, 0, 0), (0, 0, 1)].

  4. A dict containing keys 'vmin', 'vmax', 'colormap' with

    values used to render the binarized curvature (where 0 is gyral, 1 is sulcal).

Changed in version 0.24: Add support for non-string arguments.

alphafloat in [0, 1]

Alpha level to control opacity of the cortical surface.

sizeint | array-like, shape (2,)

The size of the window, in pixels. can be one number to specify a square window, or a length-2 sequence to specify (width, height).

backgroundtuple(int, int, int)

The color definition of the background: (red, green, blue).

foregroundmatplotlib color

Color of the foreground (will be used for colorbars and text). None (default) will use black or white depending on the value of background.

figurelist of Figure | None

If None (default), a new window will be created with the appropriate views.

subjects_dirstr | None

If not None, this directory will be used as the subjects directory instead of the value set using the SUBJECTS_DIR environment variable.

viewsstr | list

View to use. Using multiple views (list) is not supported for mpl backend. See Brain.show_view for valid string options.

offsetbool | str

If True, shifts the right- or left-most x coordinate of the left and right surfaces, respectively, to be at zero. This is useful for viewing inflated surface where hemispheres typically overlap. Can be “auto” (default) use True with inflated surfaces and False otherwise (Default: ‘auto’). Only used when hemi='both'.

Changed in version 0.23: Default changed to “auto”.

show_toolbarbool

If True, toolbars will be shown for each view.

offscreenbool

If True, rendering will be done offscreen (not shown). Useful mostly for generating images or screenshots, but can be buggy. Use at your own risk.

interactionstr

Can be “trackball” (default) or “terrain”, i.e. a turntable-style camera.

unitsstr

Can be ‘m’ or ‘mm’ (default).

view_layoutstr

Can be “vertical” (default) or “horizontal”. When using “horizontal” mode, the PyVista backend must be used and hemi cannot be “split”.

silhouettedict | bool

As a dict, it contains the color, linewidth, alpha opacity and decimate (level of decimation between 0 and 1 or None) of the brain’s silhouette to display. If True, the default values are used and if False, no silhouette will be displayed. Defaults to False.

themestr | path-like

Can be “auto”, “light”, or “dark” or a path-like to a custom stylesheet. For Dark-Mode and automatic Dark-Mode-Detection, qdarkstyle and darkdetect, respectively, are required. If None (default), the config option MNE_3D_OPTION_THEME will be used, defaulting to “auto” if it’s not found.

showbool

Display the window as soon as it is ready. Defaults to True.

blockbool

If True, start the Qt application event loop. Default to False.

Notes

This table shows the capabilities of each Brain backend (”✓” for full support, and “-” for partial support):

3D function:

surfer.Brain

mne.viz.Brain

add_annotation()

add_data()

add_dipole()

add_foci()

add_forward()

add_head()

add_label()

add_sensors()

add_skull()

add_text()

add_volume_labels()

close()

data

foci

labels

remove_data()

remove_dipole()

remove_forward()

remove_head()

remove_labels()

remove_annotations()

remove_sensors()

remove_skull()

remove_text()

remove_volume_labels()

save_image()

save_movie()

screenshot()

show_view()

TimeViewer

get_picked_points()

add_data(volume)

view_layout

flatmaps

vertex picking

label picking

Attributes
geodict

A dictionary of PyVista surface objects for each hemisphere.

overlaysdict

The overlays.

Methods

add_annotation(annot[, borders, alpha, ...])

Add an annotation file.

add_data(array[, fmin, fmid, fmax, thresh, ...])

Display data from a numpy array on the surface or volume.

add_dipole(dipole, trans[, colors, alpha, ...])

Add a quiver to render positions of dipoles.

add_foci(coords[, coords_as_verts, ...])

Add spherical foci, possibly mapping to displayed surf.

add_forward(fwd, trans[, alpha, scale])

Add a quiver to render positions of dipoles.

add_head([dense, color, alpha])

Add a mesh to render the outer head surface.

add_label(label[, color, alpha, ...])

Add an ROI label to the image.

add_sensors(info, trans[, meg, eeg, fnirs, ...])

Add mesh objects to represent sensor positions.

add_skull([outer, color, alpha])

Add a mesh to render the skull surface.

add_text(x, y, text[, name, color, opacity, ...])

Add a text to the visualization.

add_volume_labels([aseg, labels, colors, ...])

Add labels to the rendering from an anatomical segmentation.

apply_auto_scaling()

Detect automatically fitting scaling parameters.

clear_glyphs()

Clear the picking glyphs.

close()

Close all figures and cleanup data structure.

get_picked_points()

Return the vertices of the picked points.

help()

Display the help window.

plot_time_course(hemi, vertex_id, color[, ...])

Plot the vertex time course.

plot_time_line([update])

Add the time line to the MPL widget.

remove_annotations()

Remove all annotations from the image.

remove_data()

Remove rendered data from the mesh.

remove_dipole()

Remove dipole objects from the rendered scene.

remove_forward()

Remove forward sources from the rendered scene.

remove_head()

Remove head objects from the rendered scene.

remove_labels()

Remove all the ROI labels from the image.

remove_sensors([kind])

Remove sensors from the rendered scene.

remove_skull()

Remove skull objects from the rendered scene.

remove_text([name])

Remove text from the rendered scene.

remove_volume_labels()

Remove the volume labels from the rendered scene.

reset()

Reset view and time step.

reset_view()

Reset the camera.

restore_user_scaling()

Restore original scaling parameters.

save_image([filename, mode])

Save view from all panels to disk.

save_movie([filename, time_dilation, tmin, ...])

Save a movie (for data with a time axis).

screenshot([mode, time_viewer])

Generate a screenshot of current view.

set_data_smoothing(n_steps)

Set the number of smoothing steps.

set_playback_speed(speed)

Set the time playback speed.

set_time(time)

Set the time to display (in seconds).

set_time_interpolation(interpolation)

Set the interpolation mode.

set_time_point(time_idx)

Set the time point shown (can be a float to interpolate).

setup_time_viewer([time_viewer, show_traces])

Configure the time viewer parameters.

show()

Display the window.

show_view([view, roll, distance, row, col, ...])

Orient camera to display view.

toggle_interface([value])

Toggle the interface.

toggle_playback([value])

Toggle time playback.

update_lut([fmin, fmid, fmax, alpha])

Update color map.

add_annotation(annot, borders=True, alpha=1, hemi=None, remove_existing=True, color=None)[source]#

Add an annotation file.

Parameters
annotstr | tuple

Either path to annotation file or annotation name. Alternatively, the annotation can be specified as a (labels, ctab) tuple per hemisphere, i.e. annot=(labels, ctab) for a single hemisphere or annot=((lh_labels, lh_ctab), (rh_labels, rh_ctab)) for both hemispheres. labels and ctab should be arrays as returned by nibabel.freesurfer.io.read_annot().

bordersbool | int

Show only label borders. If int, specify the number of steps (away from the true border) along the cortical mesh to include as part of the border definition.

alphafloat in [0, 1]

Alpha level to control opacity. Default is 1.

hemistr | None

If None, it is assumed to belong to the hemipshere being shown. If two hemispheres are being shown, data must exist for both hemispheres.

remove_existingbool

If True (default), remove old annotations.

colormatplotlib-style color code

If used, show all annotations in the same (specified) color. Probably useful only when showing annotation borders.

Examples using add_annotation:

FreeSurfer MRI reconstruction

FreeSurfer MRI reconstruction

FreeSurfer MRI reconstruction
Visualize source time courses (stcs)

Visualize source time courses (stcs)

Visualize source time courses (stcs)
Plot a cortical parcellation

Plot a cortical parcellation

Plot a cortical parcellation
add_data(array, fmin=None, fmid=None, fmax=None, thresh=None, center=None, transparent=False, colormap='auto', alpha=1, vertices=None, smoothing_steps=None, time=None, time_label='auto', colorbar=True, hemi=None, remove_existing=None, time_label_size=None, initial_time=None, scale_factor=None, vector_alpha=None, clim=None, src=None, volume_options=0.4, colorbar_kwargs=None, verbose=None)[source]#

Display data from a numpy array on the surface or volume.

This provides a similar interface to surfer.Brain.add_overlay(), but it displays it with a single colormap. It offers more flexibility over the colormap, and provides a way to display four-dimensional data (i.e., a timecourse) or five-dimensional data (i.e., a vector-valued timecourse).

Note

fmin sets the low end of the colormap, and is separate from thresh (this is a different convention from surfer.Brain.add_overlay()).

Parameters
arraynumpy array, shape (n_vertices[, 3][, n_times])

Data array. For the data to be understood as vector-valued (3 values per vertex corresponding to X/Y/Z surface RAS), then array must be have all 3 dimensions. If vectors with no time dimension are desired, consider using a singleton (e.g., np.newaxis) to create a “time” dimension and pass time_label=None (vector values are not supported).

fminfloat

Minimum value in colormap (uses real fmin if None).

fmidfloat

Intermediate value in colormap (fmid between fmin and fmax if None).

fmaxfloat

Maximum value in colormap (uses real max if None).

threshNone or float

Not supported yet. If not None, values below thresh will not be visible.

centerfloat or None

If not None, center of a divergent colormap, changes the meaning of fmin, fmax and fmid.

transparentbool | None

If True: use a linear transparency between fmin and fmid and make values below fmin fully transparent (symmetrically for divergent colormaps). None will choose automatically based on colormap type.

colormapstr, list of color, or array

Name of matplotlib colormap to use, a list of matplotlib colors, or a custom look up table (an n x 4 array coded with RBGA values between 0 and 255), the default “auto” chooses a default divergent colormap, if “center” is given (currently “icefire”), otherwise a default sequential colormap (currently “rocket”).

alphafloat in [0, 1]

Alpha level to control opacity of the overlay.

verticesnumpy array

Vertices for which the data is defined (needed if len(data) < nvtx).

smoothing_stepsint or None

Number of smoothing steps (smoothing is used if len(data) < nvtx) The value ‘nearest’ can be used too. None (default) will use as many as necessary to fill the surface.

timenumpy array

Time points in the data array (if data is 2D or 3D).

time_labelstr | callable() | None

Format of the time label (a format string, a function that maps floating point time values to strings, or None for no label). The default is 'auto', which will use time=%0.2f ms if there is more than one time point.

colorbarbool

Whether to add a colorbar to the figure. Can also be a tuple to give the (row, col) index of where to put the colorbar.

hemistr | None

If None, it is assumed to belong to the hemisphere being shown. If two hemispheres are being shown, an error will be thrown.

remove_existingbool

Not supported yet. Remove surface added by previous “add_data” call. Useful for conserving memory when displaying different data in a loop.

time_label_sizeint

Font size of the time label (default 14).

initial_timefloat | None

Time initially shown in the plot. None to use the first time sample (default).

scale_factorfloat | None (default)

The scale factor to use when displaying glyphs for vector-valued data.

vector_alphafloat | None

Alpha level to control opacity of the arrows. Only used for vector-valued data. If None (default), alpha is used.

climdict

Original clim arguments.

srcinstance of SourceSpaces | None

The source space corresponding to the source estimate. Only necessary if the STC is a volume or mixed source estimate.

volume_optionsfloat | dict | None

Options for volumetric source estimate plotting, with key/value pairs:

  • 'resolution'float | None

    Resolution (in mm) of volume rendering. Smaller (e.g., 1.) looks better at the cost of speed. None (default) uses the volume source space resolution, which is often something like 7 or 5 mm, without resampling.

  • 'blending'str

    Can be “mip” (default) for maximum intensity projection or “composite” for composite blending using alpha values.

  • 'alpha'float | None

    Alpha for the volumetric rendering. Defaults are 0.4 for vector source estimates and 1.0 for scalar source estimates.

  • 'surface_alpha'float | None

    Alpha for the surface enclosing the volume(s). None (default) will use half the volume alpha. Set to zero to avoid plotting the surface.

  • 'silhouette_alpha'float | None

    Alpha for a silhouette along the outside of the volume. None (default) will use 0.25 * surface_alpha.

  • 'silhouette_linewidth'float

    The line width to use for the silhouette. Default is 2.

A float input (default 1.) or None will be used for the 'resolution' entry.

colorbar_kwargsdict | None

Options to pass to pyvista.Plotter.add_scalar_bar() (e.g., dict(title_font_size=10)).

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.

Notes

If the data is defined for a subset of vertices (specified by the “vertices” parameter), a smoothing method is used to interpolate the data onto the high resolution surface. If the data is defined for subsampled version of the surface, smoothing_steps can be set to None, in which case only as many smoothing steps are applied until the whole surface is filled with non-zeros.

Due to a VTK alpha rendering bug, vector_alpha is clamped to be strictly < 1.

Examples using add_data:

Plotting with ``mne.viz.Brain``

Plotting with mne.viz.Brain

Plotting with ``mne.viz.Brain``
add_dipole(dipole, trans, colors='red', alpha=1, scales=None)[source]#

Add a quiver to render positions of dipoles.

Parameters
dipoleinstance of Dipole

Dipole object containing position, orientation and amplitude of one or more dipoles or in the forward solution.

transstr | dict | instance of Transform

If str, the path to the head<->MRI transform *-trans.fif file produced during coregistration. Can also be 'fsaverage' to use the built-in fsaverage transformation.

colorslist | matplotlib-style color | None

A single color or list of anything matplotlib accepts: string, RGB, hex, etc. Default red.

alphafloat in [0, 1]

Alpha level to control opacity. Default 1.

scaleslist | float | None

The size of the arrow representing the dipole in mne.viz.Brain units. Default 5mm.

Notes

New in version 1.0.

Examples using add_dipole:

Plotting with ``mne.viz.Brain``

Plotting with mne.viz.Brain

Plotting with ``mne.viz.Brain``
add_foci(coords, coords_as_verts=False, map_surface=None, scale_factor=1, color='white', alpha=1, name=None, hemi=None, resolution=50)[source]#

Add spherical foci, possibly mapping to displayed surf.

The foci spheres can be displayed at the coordinates given, or mapped through a surface geometry. In other words, coordinates from a volume-based analysis in MNI space can be displayed on an inflated average surface by finding the closest vertex on the white surface and mapping to that vertex on the inflated mesh.

Parameters
coordsndarray, shape (n_coords, 3)

Coordinates in stereotaxic space (default) or array of vertex ids (with coord_as_verts=True).

coords_as_vertsbool

Whether the coords parameter should be interpreted as vertex ids.

map_surfacestr | None

Surface to project the coordinates to, or None to use raw coords. When set to a surface, each foci is positioned at the closest vertex in the mesh.

scale_factorfloat

Controls the size of the foci spheres (relative to 1cm).

colorcolor

A list of anything matplotlib accepts: string, RGB, hex, etc.

alphafloat in [0, 1]

Alpha level to control opacity. Default is 1.

namestr

Internal name to use.

hemistr | None

If None, it is assumed to belong to the hemipshere being shown. If two hemispheres are being shown, an error will be thrown.

resolutionint

The resolution of the spheres.

Examples using add_foci:

How MNE uses FreeSurfer's outputs

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs
The SourceEstimate data structure

The SourceEstimate data structure

The SourceEstimate data structure
Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA
DICS for power mapping

DICS for power mapping

DICS for power mapping
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers
add_forward(fwd, trans, alpha=1, scale=None)[source]#

Add a quiver to render positions of dipoles.

Parameters
fwdinstance of Forward

The forward solution. If present, the orientations of the dipoles present in the forward solution are displayed.

transstr | dict | instance of Transform

If str, the path to the head<->MRI transform *-trans.fif file produced during coregistration. Can also be 'fsaverage' to use the built-in fsaverage transformation.

alphafloat in [0, 1]

Alpha level to control opacity. Default 1.

scaleNone | float

The size of the arrow representing the dipoles in mne.viz.Brain units. Default 1.5mm.

Notes

New in version 1.0.

add_head(dense=True, color='gray', alpha=0.5)[source]#

Add a mesh to render the outer head surface.

Parameters
densebool

Whether to plot the dense head (seghead) or the less dense head (head).

colorcolor

A list of anything matplotlib accepts: string, RGB, hex, etc.

alphafloat in [0, 1]

Alpha level to control opacity.

Notes

New in version 0.24.

Examples using add_head:

Importing data from fNIRS devices

Importing data from fNIRS devices

Importing data from fNIRS devices
Plotting with ``mne.viz.Brain``

Plotting with mne.viz.Brain

Plotting with ``mne.viz.Brain``
add_label(label, color=None, alpha=1, scalar_thresh=None, borders=False, hemi=None, subdir=None, reset_camera=True)[source]#

Add an ROI label to the image.

Parameters
labelstr | instance of Label

Label filepath or name. Can also be an instance of an object with attributes “hemi”, “vertices”, “name”, and optionally “color” and “values” (if scalar_thresh is not None).

colormatplotlib-style color | None

Anything matplotlib accepts: string, RGB, hex, etc. (default “crimson”).

alphafloat in [0, 1]

Alpha level to control opacity.

scalar_threshNone | float

Threshold the label ids using this value in the label file’s scalar field (i.e. label only vertices with scalar >= thresh).

bordersbool | int

Show only label borders. If int, specify the number of steps (away from the true border) along the cortical mesh to include as part of the border definition.

hemistr | None

If None, it is assumed to belong to the hemipshere being shown.

subdirNone | str

If a label is specified as name, subdir can be used to indicate that the label file is in a sub-directory of the subject’s label directory rather than in the label directory itself (e.g. for $SUBJECTS_DIR/$SUBJECT/label/aparc/lh.cuneus.label brain.add_label('cuneus', subdir='aparc')).

reset_camerabool

If True, reset the camera view after adding the label. Defaults to True.

Notes

To remove previously added labels, run Brain.remove_labels().

Examples using add_label:

Plotting with ``mne.viz.Brain``

Plotting with mne.viz.Brain

Plotting with ``mne.viz.Brain``
Plot a cortical parcellation

Plot a cortical parcellation

Plot a cortical parcellation
Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs
Generate a functional label from source estimates

Generate a functional label from source estimates

Generate a functional label from source estimates
Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior
add_sensors(info, trans, meg=None, eeg='original', fnirs=True, ecog=True, seeg=True, dbs=True, verbose=None)[source]#

Add mesh objects to represent sensor positions.

Parameters
infomne.Info

The mne.Info object with information about the sensors and methods of measurement.

transstr | dict | instance of Transform

If str, the path to the head<->MRI transform *-trans.fif file produced during coregistration. Can also be 'fsaverage' to use the built-in fsaverage transformation.

megstr | list | bool | None

Can be “helmet”, “sensors” or “ref” to show the MEG helmet, sensors or reference sensors respectively, or a combination like ('helmet', 'sensors') (same as None, default). True translates to ('helmet', 'sensors', 'ref').

eegbool | str | list

String options are:

  • “original” (default; equivalent to True)

    Shows EEG sensors using their digitized locations (after transformation to the chosen coord_frame)

  • “projected”

    The EEG locations projected onto the scalp, as is done in forward modeling

Can also be a list of these options, or an empty list ([], equivalent of False).

fnirsstr | list | bool | None

Can be “channels”, “pairs”, “detectors”, and/or “sources” to show the fNIRS channel locations, optode locations, or line between source-detector pairs, or a combination like ('pairs', 'channels'). True translates to ('pairs',).

ecogbool

If True (default), show ECoG sensors.

seegbool

If True (default), show sEEG electrodes.

dbsbool

If True (default), show DBS (deep brain stimulation) electrodes.

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.

Notes

New in version 0.24.

Examples using add_sensors:

Importing data from fNIRS devices

Importing data from fNIRS devices

Importing data from fNIRS devices
Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data
Locating intracranial electrode contacts

Locating intracranial electrode contacts

Locating intracranial electrode contacts
Working with ECoG data

Working with ECoG data

Working with ECoG data
Plotting with ``mne.viz.Brain``

Plotting with mne.viz.Brain

Plotting with ``mne.viz.Brain``
add_skull(outer=True, color='gray', alpha=0.5)[source]#

Add a mesh to render the skull surface.

Parameters
outerbool

Adds the outer skull if True, otherwise adds the inner skull.

colorcolor

A list of anything matplotlib accepts: string, RGB, hex, etc.

alphafloat in [0, 1]

Alpha level to control opacity.

Notes

New in version 0.24.

add_text(x, y, text, name=None, color=None, opacity=1.0, row=0, col=0, font_size=None, justification=None)[source]#

Add a text to the visualization.

Parameters
xfloat

X coordinate.

yfloat

Y coordinate.

textstr

Text to add.

namestr

Name of the text (text label can be updated using update_text()).

colortuple

Color of the text. Default is the foreground color set during initialization (default is black or white depending on the background color).

opacityfloat

Opacity of the text (default 1.0).

rowint | None

Row index of which brain to use. Default is the top row.

colint | None

Column index of which brain to use. Default is the left-most column.

font_sizefloat | None

The font size to use.

justificationstr | None

The text justification.

Examples using add_text:

The SourceEstimate data structure

The SourceEstimate data structure

The SourceEstimate data structure
Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA
Computing various MNE solutions

Computing various MNE solutions

Computing various MNE solutions
Display sensitivity maps for EEG and MEG sensors

Display sensitivity maps for EEG and MEG sensors

Display sensitivity maps for EEG and MEG sensors
Morph surface source estimate

Morph surface source estimate

Morph surface source estimate
Visualize source leakage among labels using a circular graph

Visualize source leakage among labels using a circular graph

Visualize source leakage among labels using a circular graph
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers
Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG
add_volume_labels(aseg='aparc+aseg', labels=None, colors=None, alpha=0.5, smooth=0.9, fill_hole_size=None, legend=None)[source]#

Add labels to the rendering from an anatomical segmentation.

Parameters
asegstr

The anatomical segmentation file. Default aparc+aseg. This may be any anatomical segmentation file in the mri subdirectory of the Freesurfer subject directory.

labelslist

Labeled regions of interest to plot. See mne.get_montage_volume_labels() for one way to determine regions of interest. Regions can also be chosen from the FreeSurfer LUT.

colorslist | matplotlib-style color | None

A list of anything matplotlib accepts: string, RGB, hex, etc. (default FreeSurfer LUT colors).

alphafloat in [0, 1]

Alpha level to control opacity.

smoothfloat in [0, 1)

The smoothing factor to be applied. Default 0 is no smoothing.

fill_hole_sizeint | None

The size of holes to remove in the mesh in voxels. Default is None, no holes are removed. Warning, this dilates the boundaries of the surface by fill_hole_size number of voxels so use the minimal size.

legendbool | None | dict

Add a legend displaying the names of the labels. Default (None) is True if the number of labels is 10 or fewer. Can also be a dict of kwargs to pass to pyvista.Plotter.add_legend().

Notes

New in version 0.24.

Examples using add_volume_labels:

Visualize source time courses (stcs)

Visualize source time courses (stcs)

Visualize source time courses (stcs)
Working with sEEG data

Working with sEEG data

Working with sEEG data
apply_auto_scaling()[source]#

Detect automatically fitting scaling parameters.

clear_glyphs()[source]#

Clear the picking glyphs.

close()[source]#

Close all figures and cleanup data structure.

Examples using close:

Make figures more publication ready

Make figures more publication ready

Make figures more publication ready
property data#

Data used by time viewer and color bar widgets.

get_picked_points()[source]#

Return the vertices of the picked points.

Returns
pointslist of int | None

The vertices picked by the time viewer.

help()[source]#

Display the help window.

property interaction#

The interaction style.

plot_time_course(hemi, vertex_id, color, update=True)[source]#

Plot the vertex time course.

Parameters
hemistr

The hemisphere id of the vertex.

vertex_idint

The vertex identifier in the mesh.

colormatplotlib color

The color of the time course.

updatebool

Force an update of the plot. Defaults to True.

Returns
linematplotlib object

The time line object.

plot_time_line(update=True)[source]#

Add the time line to the MPL widget.

Parameters
updatebool

Force an update of the plot. Defaults to True.

remove_annotations()[source]#

Remove all annotations from the image.

remove_data()[source]#

Remove rendered data from the mesh.

remove_dipole()[source]#

Remove dipole objects from the rendered scene.

remove_forward()[source]#

Remove forward sources from the rendered scene.

remove_head()[source]#

Remove head objects from the rendered scene.

remove_labels()[source]#

Remove all the ROI labels from the image.

remove_sensors(kind=None)[source]#

Remove sensors from the rendered scene.

Parameters
kindstr | list | None

If None, removes all sensor-related data including the helmet. Can be “meg”, “eeg”, “fnirs”, “ecog”, “seeg”, “dbs” or “helmet” to remove that item.

remove_skull()[source]#

Remove skull objects from the rendered scene.

remove_text(name=None)[source]#

Remove text from the rendered scene.

Parameters
namestr | None

Remove specific text by name. If None, all text will be removed.

remove_volume_labels()[source]#

Remove the volume labels from the rendered scene.

reset()[source]#

Reset view and time step.

reset_view()[source]#

Reset the camera.

restore_user_scaling()[source]#

Restore original scaling parameters.

save_image(filename=None, mode='rgb')[source]#

Save view from all panels to disk.

Parameters
filenamestr

Path to new image file.

modestr

Either ‘rgb’ or ‘rgba’ for values to return.

Examples using save_image:

Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering
save_movie(filename=None, time_dilation=4.0, tmin=None, tmax=None, framerate=24, interpolation=None, codec=None, bitrate=None, callback=None, time_viewer=False, **kwargs)[source]#

Save a movie (for data with a time axis).

The movie is created through the imageio module. The format is determined by the extension, and additional options can be specified through keyword arguments that depend on the format, see imageio’s format page.

Warning

This method assumes that time is specified in seconds when adding data. If time is specified in milliseconds this will result in movies 1000 times longer than expected.

Parameters
filenamestr

Path at which to save the movie. The extension determines the format (e.g., '*.mov', '*.gif', …; see the imageio documentation for available formats).

time_dilationfloat

Factor by which to stretch time (default 4). For example, an epoch from -100 to 600 ms lasts 700 ms. With time_dilation=4 this would result in a 2.8 s long movie.

tminfloat

First time point to include (default: all data).

tmaxfloat

Last time point to include (default: all data).

frameratefloat

Framerate of the movie (frames per second, default 24).

interpolationstr | None

Interpolation method (scipy.interpolate.interp1d parameter). Must be one of ‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, or ‘cubic’. If None, it uses the current brain.interpolation, which defaults to 'nearest'. Defaults to None.

codecstr | None

The codec to use.

bitratefloat | None

The bitrate to use.

callbackcallable() | None

A function to call on each iteration. Useful for status message updates. It will be passed keyword arguments frame and n_frames.

time_viewerbool

If True, include time viewer traces. Only used if time_viewer=True and separate_canvas=False.

**kwargsdict

Specify additional options for imageio.

screenshot(mode='rgb', time_viewer=False)[source]#

Generate a screenshot of current view.

Parameters
modestr

Either ‘rgb’ or ‘rgba’ for values to return.

time_viewerbool

If True, include time viewer traces. Only used if time_viewer=True and separate_canvas=False.

Returns
screenshotarray

Image pixel values.

Examples using screenshot:

Plotting with ``mne.viz.Brain``

Plotting with mne.viz.Brain

Plotting with ``mne.viz.Brain``
Make figures more publication ready

Make figures more publication ready

Make figures more publication ready
set_data_smoothing(n_steps)[source]#

Set the number of smoothing steps.

Parameters
n_stepsint

Number of smoothing steps.

set_playback_speed(speed)[source]#

Set the time playback speed.

Parameters
speedfloat

The speed of the playback.

set_time(time)[source]#

Set the time to display (in seconds).

Parameters
timefloat

The time to show, in seconds.

set_time_interpolation(interpolation)[source]#

Set the interpolation mode.

Parameters
interpolationstr | None

Interpolation method (scipy.interpolate.interp1d parameter). Must be one of ‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, or ‘cubic’.

set_time_point(time_idx)[source]#

Set the time point shown (can be a float to interpolate).

Parameters
time_idxint | float

The time index to use. Can be a float to use interpolation between indices.

setup_time_viewer(time_viewer=True, show_traces=True)[source]#

Configure the time viewer parameters.

Parameters
time_viewerbool

If True, enable widgets interaction. Defaults to True.

show_tracesbool

If True, enable visualization of time traces. Defaults to True.

Notes

The keyboard shortcuts are the following:

‘?’: Display help window ‘i’: Toggle interface ‘s’: Apply auto-scaling ‘r’: Restore original clim ‘c’: Clear all traces ‘n’: Shift the time forward by the playback speed ‘b’: Shift the time backward by the playback speed ‘Space’: Start/Pause playback ‘Up’: Decrease camera elevation angle ‘Down’: Increase camera elevation angle ‘Left’: Decrease camera azimuth angle ‘Right’: Increase camera azimuth angle

show()[source]#

Display the window.

show_view(view=None, roll=None, distance=None, *, row=None, col=None, hemi=None, align=True, azimuth=None, elevation=None, focalpoint=None)[source]#

Orient camera to display view.

Parameters
viewstr | None

The name of the view to show (e.g. “lateral”). Other arguments take precedence and modify the camera starting from the view. See Brain.show_view for valid string shortcut options.

rollfloat | None

The roll of the camera rendering the view in degrees.

distancefloat | None

The distance from the camera rendering the view to the focalpoint in plot units (either m or mm).

rowint | None

The row to set. Default all rows.

colint | None

The column to set. Default all columns.

hemistr | None

Which hemi to use for view lookup (when in “both” mode).

alignbool

If True, consider view arguments relative to canonical MRI directions (closest to MNI for the subject) rather than native MRI space. This helps when MRIs are not in standard orientation (e.g., have large rotations).

azimuthfloat

The azimuthal angle of the camera rendering the view in degrees.

elevationfloat

The The zenith angle of the camera rendering the view in degrees.

focalpointtuple, shape (3,) | None

The focal point of the camera rendering the view: (x, y, z) in plot units (either m or mm).

Notes

The builtin string views are the following perspectives, based on the RAS convention. If not otherwise noted, the view will have the top of the brain (superior, +Z) in 3D space shown upward in the 2D perspective:

'lateral'

From the left or right side such that the lateral (outside) surface of the given hemisphere is visible.

'medial'

From the left or right side such that the medial (inside) surface of the given hemisphere is visible (at least when in split or single-hemi mode).

'rostral'

From the front.

'caudal'

From the rear.

'dorsal'

From above, with the front of the brain pointing up.

'ventral'

From below, with the front of the brain pointing up.

'frontal'

From the front and slightly lateral, with the brain slightly tilted forward (yielding a view from slightly above).

'parietal'

From the rear and slightly lateral, with the brain slightly tilted backward (yielding a view from slightly above).

'axial'

From above with the brain pointing up (same as 'dorsal').

'sagittal'

From the right side.

'coronal'

From the rear.

Three letter abbreviations (e.g., 'lat') of all of the above are also supported.

Examples using show_view:

Importing data from fNIRS devices

Importing data from fNIRS devices

Importing data from fNIRS devices
Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data
How MNE uses FreeSurfer's outputs

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs
Visualize source time courses (stcs)

Visualize source time courses (stcs)

Visualize source time courses (stcs)
Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering
Locating intracranial electrode contacts

Locating intracranial electrode contacts

Locating intracranial electrode contacts
Working with sEEG data

Working with sEEG data

Working with sEEG data
DICS for power mapping

DICS for power mapping

DICS for power mapping
Plotting with ``mne.viz.Brain``

Plotting with mne.viz.Brain

Plotting with ``mne.viz.Brain``
Generate a functional label from source estimates

Generate a functional label from source estimates

Generate a functional label from source estimates
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
property time_interpolation#

The interpolation mode.

toggle_interface(value=None)[source]#

Toggle the interface.

Parameters
valuebool | None

If True, the widgets are shown and if False, they are hidden. If None, the state of the widgets is toggled. Defaults to None.

toggle_playback(value=None)[source]#

Toggle time playback.

Parameters
valuebool | None

If True, automatic time playback is enabled and if False, it’s disabled. If None, the state of time playback is toggled. Defaults to None.

update_lut(fmin=None, fmid=None, fmax=None, alpha=None)[source]#

Update color map.

Parameters
fminfloat

Minimum value in colormap (uses real fmin if None).

fmidfloat

Intermediate value in colormap (fmid between fmin and fmax if None).

fmaxfloat

Maximum value in colormap (uses real max if None).

alphafloat in [0, 1]

Alpha level to control opacity.

Examples using mne.viz.Brain#

Importing data from fNIRS devices

Importing data from fNIRS devices

Importing data from fNIRS devices
Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset
Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data
FreeSurfer MRI reconstruction

FreeSurfer MRI reconstruction

FreeSurfer MRI reconstruction
How MNE uses FreeSurfer's outputs

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs
The SourceEstimate data structure

The SourceEstimate data structure

The SourceEstimate data structure
Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA
The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization
Computing various MNE solutions

Computing various MNE solutions

Computing various MNE solutions
Source reconstruction using an LCMV beamformer

Source reconstruction using an LCMV beamformer

Source reconstruction using an LCMV beamformer
Visualize source time courses (stcs)

Visualize source time courses (stcs)

Visualize source time courses (stcs)
EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI
Permutation t-test on source data with spatio-temporal clustering

Permutation t-test on source data with spatio-temporal clustering

Permutation t-test on source data with spatio-temporal clustering
2 samples permutation test on source data with spatio-temporal clustering

2 samples permutation test on source data with spatio-temporal clustering

2 samples permutation test on source data with spatio-temporal clustering
Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering
Decoding (MVPA)

Decoding (MVPA)

Decoding (MVPA)
Locating intracranial electrode contacts

Locating intracranial electrode contacts

Locating intracranial electrode contacts
Working with sEEG data

Working with sEEG data

Working with sEEG data
Working with ECoG data

Working with ECoG data

Working with ECoG data
Corrupt known signal with point spread

Corrupt known signal with point spread

Corrupt known signal with point spread
DICS for power mapping

DICS for power mapping

DICS for power mapping
Simulate raw data using subject anatomy

Simulate raw data using subject anatomy

Simulate raw data using subject anatomy
Plotting with ``mne.viz.Brain``

Plotting with mne.viz.Brain

Plotting with ``mne.viz.Brain``
Plot a cortical parcellation

Plot a cortical parcellation

Plot a cortical parcellation
Make figures more publication ready

Make figures more publication ready

Make figures more publication ready
Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs
Compute source power spectral density (PSD) of VectorView and OPM data

Compute source power spectral density (PSD) of VectorView and OPM data

Compute source power spectral density (PSD) of VectorView and OPM data
Decoding source space data

Decoding source space data

Decoding source space data
Display sensitivity maps for EEG and MEG sensors

Display sensitivity maps for EEG and MEG sensors

Display sensitivity maps for EEG and MEG sensors
Use source space morphing

Use source space morphing

Use source space morphing
Compute source power using DICS beamformer

Compute source power using DICS beamformer

Compute source power using DICS beamformer
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Generate a functional label from source estimates

Generate a functional label from source estimates

Generate a functional label from source estimates
Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space
Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE
Morph surface source estimate

Morph surface source estimate

Morph surface source estimate
Visualize source leakage among labels using a circular graph

Visualize source leakage among labels using a circular graph

Visualize source leakage among labels using a circular graph
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers
Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG
Computing source space SNR

Computing source space SNR

Computing source space SNR
Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior
Plotting the full vector-valued MNE solution

Plotting the full vector-valued MNE solution

Plotting the full vector-valued MNE solution
Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data