mne.viz.Brain #

FreeSurfer MRI reconstruction

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 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.

Examples using add_data:

add_dipole(dipole, trans, colors='red', alpha=1, scales=None, *, mode='arrow')[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.
mode“2darrow” | “arrow” | “cone” | “cylinder” | “sphere” | “oct”: The drawing mode for the dipole to render. Defaults to "arrow".

Notes

New in v1.0.

Examples using add_dipole:

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

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 hemisphere being shown. If two hemispheres are being shown, an error will be thrown.
resolutionint: The resolution of the spheres.

Examples using add_foci:

Compute cross-talk functions for LCMV beamformers

Plot point-spread functions (PSFs) for a volume

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs

The SourceEstimate data structure

Source localization with MNE, dSPM, sLORETA, and eLORETA

DICS for power mapping

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 v1.0.

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 v0.24.

Examples using add_head:

Generate a functional label from source estimates

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

Examples using add_label:

Compute MxNE with time-frequency sparse prior

Compute Power Spectral Density of inverse solution from single epochs

Plot a cortical parcellation

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)[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 | 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.

Examples using add_sensors:

Preprocessing functional near-infrared spectroscopy (fNIRS) data

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 v0.24.

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:

Display sensitivity maps for EEG and MEG sensors

Morph surface source estimate

Visualize source leakage among labels using a circular graph

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

Compute cross-talk functions for LCMV beamformers

Plot point-spread functions (PSFs) for a volume

Compute spatial resolution metrics in source space

Compute spatial resolution metrics to compare MEG with EEG+MEG

The SourceEstimate data structure

Source localization with MNE, dSPM, sLORETA, and eLORETA

Computing various MNE solutions

add_volume_labels(aseg='auto', 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 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.

Examples using add_volume_labels:

Make figures more publication ready

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:

property data#: Data used by time viewer and color bar widgets.

get_picked_points()[source]#

Return the vertices of the picked points.

Returns:

pointsdict | None: The vertices picked by the time viewer, one key per hemisphere with a list of vertex indices.

get_view(row=0, col=0, *, align=True)[source]#

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()[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, current time 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:

filenamepath-like: 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

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:

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.

Examples using set_time:

Using the event system to link figures

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 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)[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, update=True, verbose=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 | “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.

Examples using show_view:

Generate a functional label from source estimates

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

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs

Preprocessing functional near-infrared spectroscopy (fNIRS) data

DICS for power mapping

Repeated measures ANOVA on source data with spatio-temporal clustering

Using the event system to link figures

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 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`#

Optically pumped magnetometer (OPM) data

From raw data to dSPM on SPM Faces dataset

Decoding source space data

Display sensitivity maps for EEG and MEG sensors

Use source space morphing

Compute source level time-frequency timecourses using a DICS beamformer

Compute source power using DICS beamformer

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

Generate a functional label from source estimates

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

Compute source power estimate by projecting the covariance with MNE

Morph surface source estimate

Visualize source leakage among labels using a circular graph

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

Compute cross-talk functions for LCMV beamformers

Plot point-spread functions (PSFs) for a volume

Compute spatial resolution metrics in source space

Compute spatial resolution metrics to compare MEG with EEG+MEG

Computing source space SNR

Compute MxNE with time-frequency sparse prior

Plotting the full vector-valued MNE solution

Simulate raw data using subject anatomy

Compute Power Spectral Density of inverse solution from single epochs

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

Plot a cortical parcellation

FreeSurfer MRI reconstruction

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs

The SourceEstimate data structure

Source localization with MNE, dSPM, sLORETA, and eLORETA

The role of dipole orientations in distributed source localization

Computing various MNE solutions

Source reconstruction using an LCMV beamformer

EEG source localization given electrode locations on an MRI