def dip_source_loc(evoked, cov, fs_subj, study_path, plot=False):
evo_crop = evoked.copy().crop(-0.003, 0.003)
subj = fs_subj.strip('_an') if fs_subj.find('_an') > 0 else fs_subj
img_type = 'anony' if fs_subj.find('_an') > 0 else 'orig'
trans_fname = op.join(study_path, 'source_stim', subj, 'source_files', img_type, '%s_fid-trans.fif' % fs_subj)
bem_fname = op.join(subjects_dir, fs_subj, 'bem', '%s-bem-sol.fif' % fs_subj)
cond = evoked.info['description']
stim_coords = find_stim_coords(cond, subj, study_path)
dip, res = mne.fit_dipole(evo_crop, cov, bem_fname, trans_fname, min_dist=10, n_jobs=3)
import mne.transforms as tra
from scipy import linalg
trans = mne.read_trans(trans_fname)
stim_point = stim_coords['surf'] # get point for plot in mm
#dip.pos[np.argmax(dip.gof)] = tra.apply_trans(surf_to_head, stim_coords['surf_ori']/1e3) # check stim loc (apply affine in m)
dip_surf = tra.apply_trans(trans['trans'], dip.pos[np.argmax(dip.gof)]) * 1e3 # transform from head to surface
dist_surf = euclidean(dip_surf, stim_point) # compute distance in surface space
print(dist_surf)
if plot:
# Plot the result in 3D brain with the MRI image.
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
dip.plot_locations(trans_fname, fs_subj, subjects_dir, mode='orthoview', coord_frame='mri', ax=ax, show_all=True,
idx='gof')
ax.scatter(stim_point[0], stim_point[1], stim_point[2])
ax.plot([stim_point[0], -128], [stim_point[1], stim_point[1]], [stim_point[2], stim_point[2]], color='g')
ax.plot([stim_point[0], stim_point[0]], [stim_point[1], -128], [stim_point[2], stim_point[2]], color='g')
ax.plot([stim_point[0], stim_point[0]], [stim_point[1], stim_point[1]], [stim_point[2], -128], color='g')
ax.text2D(0.05, 0.90, 'distance: %i mm \nstim coords = %0.1f %0.1f %0.1f' % (dist_surf, stim_point[0], stim_point[1], stim_point[2]),
transform=ax.transAxes)
red_patch = mpatches.Patch(color='red')
green_patch = mpatches.Patch(color='green')
fig.legend(handles=[red_patch, green_patch], labels=['dipole', 'electrode'])
fig.savefig(op.join(study_path, 'source_stim', subj, 'figures', 'dipole', '%s_dip_15mm.png' % cond))
plt.close()
plot_dipole_amplitudes(dip)
plot_dipole_locations(dip, trans, subj, subjects_dir=subjects_dir)
return dist_surf
# Compute TF-MxNE inverse solution with dipole output
dipoles, residual = tf_mixed_norm(
evoked, forward, cov, alpha=alpha, l1_ratio=l1_ratio, loose=loose,
depth=depth, maxit=200, tol=1e-6, weights=stc_dspm, weights_min=8.,
debias=True, wsize=16, tstep=4, window=0.05, return_as_dipoles=True,
return_residual=True)
# Crop to remove edges
for dip in dipoles:
dip.crop(tmin=-0.05, tmax=0.3)
evoked.crop(tmin=-0.05, tmax=0.3)
residual.crop(tmin=-0.05, tmax=0.3)
###############################################################################
# Plot dipole activations
plot_dipole_amplitudes(dipoles)
###############################################################################
# Plot location of the strongest dipole with MRI slices
idx = np.argmax([np.max(np.abs(dip.amplitude)) for dip in dipoles])
plot_dipole_locations(dipoles[idx], forward['mri_head_t'], 'sample',
subjects_dir=subjects_dir, mode='orthoview',
idx='amplitude')
# # Plot dipole locations of all dipoles with MRI slices
# for dip in dipoles:
# plot_dipole_locations(dip, forward['mri_head_t'], 'sample',
# subjects_dir=subjects_dir, mode='orthoview',
# idx='amplitude')
###############################################################################
inverse_operator = make_inverse_operator(evoked.info, forward, cov,
depth=depth, fixed=True,
use_cps=True)
stc_dspm = apply_inverse(evoked, inverse_operator, lambda2=1. / 9.,
method='dSPM')
# Compute (ir)MxNE inverse solution with dipole output
dipoles, residual = mixed_norm(
evoked, forward, cov, alpha, loose=loose, depth=depth, maxit=3000,
tol=1e-4, active_set_size=10, debias=True, weights=stc_dspm,
weights_min=8., n_mxne_iter=n_mxne_iter, return_residual=True,
return_as_dipoles=True)
###############################################################################
# Plot dipole activations
plot_dipole_amplitudes(dipoles)
# Plot dipole location of the strongest dipole with MRI slices
idx = np.argmax([np.max(np.abs(dip.amplitude)) for dip in dipoles])
plot_dipole_locations(dipoles[idx], forward['mri_head_t'], 'sample',
subjects_dir=subjects_dir, mode='orthoview',
idx='amplitude')
# # Plot dipole locations of all dipoles with MRI slices
# for dip in dipoles:
# plot_dipole_locations(dip, forward['mri_head_t'], 'sample',
# subjects_dir=subjects_dir, mode='orthoview',
# idx='amplitude')
###############################################################################
# Plot residual
def dipole_source_loc(evoked, cov, fs_subj, study_path, plot=False):
evo_crop = evoked.copy().crop(-0.005, 0.005)
subj = fs_subj.strip('_an') if fs_subj.find('_an') > 0 else fs_subj
img_type = 'anony' if fs_subj.find('_an') > 0 else 'orig'
seeg_ch_info = pd.read_csv(
op.join(study_path, 'physio_data', subj, 'chan_info',
'%s_seeg_ch_info.csv' % subj))
# ori_to_an_fname = op.join(study_path, 'freesurfer_subjects', subj, 'mri', 'ori_to_an_trans.lta') # mri to mri
# ori_to_an_trans = np.genfromtxt(ori_to_an_fname, skip_header=8, skip_footer=18)
trans_fname = op.join(study_path, 'source_stim', subj, 'source_files',
img_type, '%s_static-trans.fif' % fs_subj)
bem_fname = op.join(subjects_dir, fs_subj, 'bem',
'%s-bem-sol.fif' % fs_subj)
cond = eeg_epo.info['description']
stim_coords = find_stim_coords(cond, subj, study_path)
# Fit a dipole
dip = mne.fit_dipole(evo_crop,
cov,
bem_fname,
trans_fname,
min_dist=5,
n_jobs=2)[0]
from mne.beamformer import rap_music
dip = rap_music(evo_crop,
fwd,
cov,
n_dipoles=1,
return_residual=True,
verbose=True)[0][0]
import mne.transforms as tra
from scipy import linalg
trans = mne.read_trans(trans_fname)
surf_to_head = linalg.inv(trans['trans'])
# stim_point = tra.apply_trans(surf_to_head, stim_coords['surf']/1e3)
stim_point = stim_coords['surf_ori'] # get point for plot in mm
#dip.pos[np.argmax(dip.gof)] = tra.apply_trans(surf_to_head, stim_coords['surf_ori']/1e3) # check stim loc (apply affine in m)
#stim_point = tra.apply_trans(ori_to_an_trans, stim_point)
dip_surf = tra.apply_trans(trans['trans'], dip.pos[np.argmax(
dip.gof)]) * 1e3 # transform from head to surface
dist_surf = euclidean(dip_surf,
stim_point) # compute distance in surface space
print(dist_surf)
if plot:
# Plot the result in 3D brain with the MRI image.
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
dip.plot_locations(trans_fname,
fs_subj,
subjects_dir,
mode='orthoview',
coord_frame='mri',
ax=ax,
show_all=True,
idx='gof')
ax.scatter(stim_point[0], stim_point[1], stim_point[2])
ax.plot([stim_point[0], -128], [stim_point[1], stim_point[1]],
[stim_point[2], stim_point[2]],
color='g')
ax.plot([stim_point[0], stim_point[0]], [stim_point[1], -128],
[stim_point[2], stim_point[2]],
color='g')
ax.plot([stim_point[0], stim_point[0]], [stim_point[1], stim_point[1]],
[stim_point[2], -128],
color='g')
ax.text2D(0.05,
0.90,
'distance: %i mm \nstim coords = %0.1f %0.1f %0.1f' %
(dist_surf, stim_point[0], stim_point[1], stim_point[2]),
transform=ax.transAxes)
red_patch = mpatches.Patch(color='red')
green_patch = mpatches.Patch(color='green')
fig.legend(handles=[red_patch, green_patch],
labels=['dipole', 'electrode'])
fig.savefig(
op.join(study_path, 'source_stim', subj, 'figures', 'dipole',
'%s_dip_15mm.png' % cond))
plt.close()
from mne.viz import plot_dipole_locations, plot_dipole_amplitudes
plot_dipole_amplitudes(dip)
plot_dipole_locations(dip, trans, subj, subjects_dir=subjects_dir)
return dist_surf
