mne.viz.Brain

class mne.viz.Brain(subject, 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', interaction='trackball', units='mm', view_layout='vertical', silhouette=False, theme=None, show=True)

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:

subjectstr

Subject name in Freesurfer subjects dir.

Changed in version 1.2: This parameter was renamed from subject_id to subject.

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”.

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.

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.
get_view([row, col, align])	Get the camera orientation for a given subplot display.
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, current time 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 to display (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 the range of the color map.

Notes

The figure will publish and subscribe to the following UI events:

• TimeChange

• PlaybackSpeed

• ColormapRange, kind="distributed_source_power"

• VertexSelect

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		✓

add_annotation(annot, borders=True, alpha=1, hemi=None, remove_existing=True, color=None)

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 hemisphere 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.

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)

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

This provides a similar interface to PySurfer, 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 PySurfer).

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.

add_dipole(dipole, trans, colors='red', alpha=1, scales=None)

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 v1.0.

add_foci(coords, coords_as_verts=False, map_surface=None, scale_factor=1, color='white', alpha=1, name=None, hemi=None, resolution=50)

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 hemisphere being shown. If two hemispheres are being shown, an error will be thrown.

resolutionint

The resolution of the spheres.

add_forward(fwd, trans, alpha=1, scale=None)

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 v1.0.

add_head(dense=True, color='gray', alpha=0.5)

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 v0.24.

add_label(label, color=None, alpha=1, scalar_thresh=None, borders=False, hemi=None, subdir=None)

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 hemisphere 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')).

Notes

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

add_sensors(info, trans, meg=None, eeg='original', fnirs=True, ecog=True, seeg=True, dbs=True, max_dist=0.004, *, sensor_colors=None, sensor_scales=None, verbose=None)

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 | dict | 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'). Can also be a dict to specify alpha values, e.g. {"helmet": 0.1, "sensors": 0.8}.

Changed in version 1.6: Added support for specifying alpha values as a dict.

eegbool | str | list | dict

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 a dict to specify the alpha values to use, e.g. dict(original=0.2, projected=0.8).

Changed in version 1.6: Added support for specifying alpha values as a dict.

fnirsstr | list | dict | 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',). A dict can also be used to specify alpha values (but only “channels” and “pairs” will be used), e.g. dict(channels=0.2, pairs=0.7).

Changed in version 1.6: Added support for specifying alpha values as a dict.

ecogbool

If True (default), show ECoG sensors.

seegbool

If True (default), show sEEG electrodes.

dbsbool

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

max_distfloat

The maximum distance to project a sensor to the pial surface in meters. Sensors that are greater than this distance from the pial surface will not be assigned locations. Projections can be done to the inflated or flat brain.

sensor_colorsarray_like of color | dict | None

Colors to use for the sensor glyphs. Can be None (default) to use default colors. A dict should provide the colors (values) for each channel type (keys), e.g.:

dict(eeg=eeg_colors)

Where the value (eeg_colors above) can be broadcast to an array of colors with length that matches the number of channels of that type, i.e., is compatible with matplotlib.colors.to_rgba_array(). A few examples of this for the case above are the string "k", a list of n_eeg color strings, or an NumPy ndarray of shape (n_eeg, 3) or (n_eeg, 4).

New in v1.6.

sensor_scalesint | float | array_like | dict | None

Scale to use for the sensor glyphs. Can be None (default) to use default scale. A dict should provide the Scale (values) for each channel type (keys), e.g.:

dict(eeg=eeg_scales)

Where the value (eeg_scales above) can be broadcast to an array of values with length that matches the number of channels of that type. A few examples of this for the case above are the value 10e-3, a list of n_eeg values, or an NumPy ndarray of shape (n_eeg,).

New in v1.9.

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 v0.24.

add_skull(outer=True, color='gray', alpha=0.5)

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 v0.24.

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

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.

add_volume_labels(aseg='auto', labels=None, colors=None, alpha=0.5, smooth=0.9, fill_hole_size=None, legend=None)

Add labels to the rendering from an anatomical segmentation.

Parameters:

asegstr

The anatomical segmentation file. Default auto uses aparc+aseg if available and wmparc if not. This may be any anatomical segmentation file in the mri subdirectory of the Freesurfer subject directory.

Changed in version 1.8: Added support for the new default 'auto'.

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 v0.24.

apply_auto_scaling()

Detect automatically fitting scaling parameters.

clear_glyphs()

Clear the picking glyphs.

close()

Close all figures and cleanup data structure.

property data

Data used by time viewer and color bar widgets.

get_picked_points()

Return the vertices of the picked points.

Returns:

pointslist of int | None

The vertices picked by the time viewer.

get_view(row=0, col=0, *, align=True)

Get the camera orientation for a given subplot display.

Parameters:

rowint

The row to use, default is the first one.

colint

The column to check, the default is the first one.

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).

Returns:

rollfloat | None

The roll of the camera rendering the view in degrees.

distancefloat | “auto” | None

The distance from the camera rendering the view to the focalpoint in plot units (either m or mm). If “auto”, the bounds of visible objects will be used to set a reasonable distance.

Changed in version 1.6: None will no longer change the distance, use "auto" instead.

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,) | str | None

The focal point of the camera rendering the view: (x, y, z) in plot units (either m or mm). When "auto", it is set to the center of mass of the visible bounds.

help()

Display the help window.

property interaction

The interaction style.

plot_time_course(hemi, vertex_id, color, update=True)

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)

Add the time line to the MPL widget.

Parameters:

updatebool

Force an update of the plot. Defaults to True.

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=None)

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()

Remove skull objects from the rendered scene.

remove_text(name=None)

Remove text from the rendered scene.

Parameters:

namestr | None

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

remove_volume_labels()

Remove the volume labels from the rendered scene.

reset()

Reset view, current time and time step.

reset_view()

Reset the camera.

restore_user_scaling()

Restore original scaling parameters.

save_image(filename=None, mode='rgb')

Save view from all panels to disk.

Parameters:

filenamepath-like

Path to new image file.

modestr

Either 'rgb' or 'rgba' for values to return.

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)

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)

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.

set_data_smoothing(n_steps)

Set the number of smoothing steps.

Parameters:

n_stepsint

Number of smoothing steps.

set_playback_speed(speed)

Set the time playback speed.

Parameters:

speedfloat

The speed of the playback.

set_time(time)

Set the time to display (in seconds).

Parameters:

timefloat

The time to show, in seconds.

set_time_interpolation(interpolation)

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)

Set the time point to display (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)

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()

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, update=True, verbose=None)

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 | “auto” | None

The distance from the camera rendering the view to the focalpoint in plot units (either m or mm). If “auto”, the bounds of visible objects will be used to set a reasonable distance.

Changed in version 1.6: None will no longer change the distance, use "auto" instead.

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,) | str | None

The focal point of the camera rendering the view: (x, y, z) in plot units (either m or mm). When "auto", it is set to the center of mass of the visible bounds.

updatebool

Force an update of the plot. Defaults to True.

New in v1.6.

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

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.

property time_interpolation

The interpolation mode.

toggle_interface(value=None)

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)

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)

Update the range of the 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

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

Visualize source time courses (stcs)

Visualize source time courses (stcs)

Working with sEEG data

Working with sEEG data

Plotting with mne.viz.Brain

Plotting with mne.viz.Brain