Source space observations

visualize_3d_head_surfaces.py

#!/usr/bin/env python

'''
Load a surface and visualize it using Mayavi.
'''

import mne
from mayavi import mlab

# The surfaces are usually located in $SUBJECTS_DIR/$SUBJECT/surf/*

# plot the inflated surface
# the read_surface returns points and triangle surfaces
inflated_points, inflated_tri = mne.read_surface('./surf/lh.inflated')

f = mlab.figure(figure='Inflated', bgcolor=(.05, 0, .1), size=(600, 600))
mlab.clf()
inflated_surf = mlab.triangular_mesh(inflated_points[:, 0],
                                     inflated_points[:, 1], inflated_points[:, 2],
                                     inflated_tri, color=(0.7,) * 3, opacity=1.)

# plot the white matter surface
white_points, white_tri = mne.read_surface('./surf/lh.white')
f = mlab.figure(figure='White', bgcolor=(.05, 0, .1), size=(600, 600))
mlab.clf()
white_surf = mlab.triangular_mesh(white_points[:, 0], white_points[:, 1],
                                  white_points[:, 2], white_tri, color=(0.7,) * 3, opacity=1.)

visualize_3d_source_space.py

#!/usr/bin/env python

'''
Function to plot the source space on a glass brain.
'''

import numpy as np
import mne

# load the source space
# usually located in $SUBJECTS_DIR/$SUBJECT/bem/*-src.fif
src = mne.read_source_spaces('/data/megraid21/sripad/ldaep01_resting_state_analysis/205726/bem/205726-ico-4-src.fif')

# Show 3D visualization of the source space

# read the coordinates of the points
lh_points = src[0]['rr']
rh_points = src[1]['rr']
points = np.r_[lh_points, rh_points]

# coordinats of normals (for later field calculations)
# not used here
lh_normals = src[0]['nn']
rh_normals = src[1]['nn']
normals = np.r_[lh_normals, rh_normals]

# read the triangular meshes
use_lh_faces = src[0]['tris']
use_rh_faces = src[1]['tris']

use_faces = np.r_[use_lh_faces, lh_points.shape[0] + use_rh_faces]

points *= 170

from mayavi import mlab
f = mlab.figure(figure='source_space', bgcolor=(.05, 0, .1), size=(600, 600))
mlab.clf()

# the opacity can be changed accordingly
import warnings
with warnings.catch_warnings(record=True):
    surface = mlab.triangular_mesh(points[:, 0], points[:, 1], points[:, 2],
                                   use_faces, color=(0.7,) * 3, opacity=1.)