def _plot_3d_evoked_array(inst,
ea,
picks="hbo",
value="Coef.",
background='w',
figure=None,
clim='auto',
mode='weighted',
colormap='RdBu_r',
surface='pial',
hemi='both',
size=800,
view=None,
colorbar=True,
distance=0.03,
subjects_dir=None,
src=None,
verbose=False):
# TODO: mimic behaviour of other MNE-NIRS glm plotting options
if picks is not None:
ea = ea.pick(picks=picks)
if subjects_dir is None:
subjects_dir = get_subjects_dir(raise_error=True)
if src is None:
fname_src_fs = os.path.join(subjects_dir, 'fsaverage', 'bem',
'fsaverage-ico-5-src.fif')
src = read_source_spaces(fname_src_fs)
picks = np.arange(len(ea.info['ch_names']))
# Set coord frame
for idx in range(len(ea.ch_names)):
ea.info['chs'][idx]['coord_frame'] = FIFF.FIFFV_COORD_HEAD
# Generate source estimate
kwargs = dict(evoked=ea,
subject='fsaverage',
trans='fsaverage',
distance=distance,
mode=mode,
surface=surface,
subjects_dir=subjects_dir,
src=src,
project=True)
stc = stc_near_sensors(picks=picks, **kwargs, verbose=verbose)
# Produce brain plot
brain = stc.plot(src=src,
subjects_dir=subjects_dir,
hemi=hemi,
surface=surface,
initial_time=0,
clim=clim,
size=size,
colormap=colormap,
figure=figure,
background=background,
colorbar=colorbar,
verbose=verbose)
if view is not None:
brain.show_view(view)
return brain
# As shown in the plot, the epileptiform activity starts in the temporal lobe,
# progressing posteriorly. The seizure becomes generalized eventually, after
# this example short time section. This dataset is available using
# :func:`mne.datasets.epilepsy_ecog.data_path` for you to examine.
# sphinx_gallery_thumbnail_number = 5
xyz_pts = np.array([dig['r'] for dig in evoked.info['dig']])
src = mne.read_source_spaces(
op.join(subjects_dir, 'fsaverage', 'bem', 'fsaverage-ico-5-src.fif'))
trans = None # identity transform
stc = mne.stc_near_sensors(gamma_power_t,
trans,
'fsaverage',
src=src,
mode='nearest',
subjects_dir=subjects_dir,
distance=0.02)
vmin, vmid, vmax = np.percentile(gamma_power_t.data, [10, 25, 90])
clim = dict(kind='value', lims=[vmin, vmid, vmax])
brain = stc.plot(surface='pial',
hemi='rh',
colormap='inferno',
colorbar=False,
clim=clim,
views=['lat', 'med'],
subjects_dir=subjects_dir,
size=(250, 250),
smoothing_steps=20,
time_viewer=False)
#
# As shown in the plot, the epileptiform activity starts in the temporal lobe,
# progressing posteriorly. The seizure becomes generalized eventually, after
# this example short time section. This dataset is available using
# :func:`mne.datasets.epilepsy_ecog.data_path` for you to examine.
# sphinx_gallery_thumbnail_number = 3
xyz_pts = np.array([dig['r'] for dig in evoked.info['dig']])
src = mne.read_source_spaces(subjects_dir / 'fsaverage' / 'bem' /
'fsaverage-ico-5-src.fif')
stc = mne.stc_near_sensors(gamma_power_t,
trans='fsaverage',
subject='fsaverage',
subjects_dir=subjects_dir,
src=src,
surface='pial',
mode='nearest',
distance=0.02)
vmin, vmid, vmax = np.percentile(gamma_power_t.data, [10, 25, 90])
clim = dict(kind='value', lims=[vmin, vmid, vmax])
brain = stc.plot(surface='pial',
hemi='rh',
colormap='inferno',
colorbar=False,
clim=clim,
views=['lat', 'med'],
subjects_dir=subjects_dir,
size=(250, 250),
smoothing_steps='nearest',
time_viewer=False)
# We can visualize this raw data on the ``fsaverage`` brain (in MNI space) as
# a heatmap. This works by first creating an ``Evoked`` data structure
# from the data of interest (in this example, it is just the raw LFP).
# Then one should generate a ``stc`` data structure, which will be able
# to visualize source activity on the brain in various different formats.
# get standard fsaverage volume (5mm grid) source space
fname_src = op.join(subjects_dir, 'fsaverage', 'bem',
'fsaverage-vol-5-src.fif')
vol_src = mne.read_source_spaces(fname_src)
evoked = epochs.average()
stc = mne.stc_near_sensors(
evoked,
trans,
'fsaverage',
subjects_dir=subjects_dir,
src=vol_src,
verbose='error') # ignore missing electrode warnings
stc = abs(stc) # just look at magnitude
clim = dict(kind='value', lims=np.percentile(abs(evoked.data), [10, 50, 75]))
# %%
# Plot 3D source (brain region) visualization:
#
# By default, `stc.plot_3d() <mne.VolSourceEstimate.plot_3d>` will show a time
# course of the source with the largest absolute value across any time point.
# In this example, it is simply the source with the largest raw signal value.
# Its location is marked on the brain by a small blue sphere.
# sphinx_gallery_thumbnail_number = 6
show_power = gamma_power_t[:, 100:150]
anim = animation.FuncAnimation(fig,
animate,
init_func=init,
fargs=(show_power, ),
frames=show_power.shape[1],
interval=200,
blit=True)
###############################################################################
# Alternatively, we can project the sensor data to the nearest locations on
# the pial surface and visualize that:
# sphinx_gallery_thumbnail_number = 4
evoked = mne.EvokedArray(gamma_power_t, raw.info)
stc = mne.stc_near_sensors(evoked, trans, subject, subjects_dir=subjects_dir)
clim = dict(kind='value', lims=[vmin * 0.9, vmin, vmax])
brain = stc.plot(surface='pial',
hemi='both',
initial_time=0.68,
colormap='viridis',
clim=clim,
views='parietal',
subjects_dir=subjects_dir,
size=(600, 600))
# You can save a movie like the one on our documentation website with:
# brain.save_movie(time_dilation=20, tmin=0.62, tmax=0.72,
# interpolation='linear', framerate=5,
# time_viewer=True)
