# Let's localize the N100m (using MEG only)
evoked = mne.read_evokeds(fname_ave, condition='Right Auditory',
baseline=(None, 0))
evoked.pick_types(meg=True, eeg=False)
evoked.crop(0.07, 0.08)
# Fit a dipole
dip = mne.fit_dipole(evoked, fname_cov, fname_bem, fname_trans)[0]
# Plot the result in 3D brain
dip.plot_locations(fname_trans, 'sample', subjects_dir)
###############################################################################
# Calculate and visualise magnetic field predicted by dipole with maximum GOF
# and compare to the measured data, highlighting the ipsilateral (right) source
fwd, stc = make_forward_dipole(dip, fname_bem, evoked.info, fname_trans)
pred_evoked = simulate_evoked(fwd, stc, evoked.info, None, snr=np.inf)
# find time point with highes GOF to plot
best_time = dip.times[np.argmax(dip.gof)]
# rememeber to create a subplot for the colorbar
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=[10., 3.4])
vmin, vmax = -400, 400 # make sure each plot has same colour range
# first plot the topography at the time of the best fitting (single) dipole
plot_params = dict(times=best_time, ch_type='mag', outlines='skirt',
colorbar=False)
evoked.plot_topomap(time_format='Measured field', axes=axes[0], **plot_params)
# compare this to the predicted field
pred_evoked.plot_topomap(time_format='Predicted field', axes=axes[1],
condition='Right Auditory',
baseline=(None, 0))
evoked.pick_types(meg=True, eeg=False)
evoked_full = evoked.copy()
evoked.crop(0.07, 0.08)
# Fit a dipole
dip = mne.fit_dipole(evoked, fname_cov, fname_bem, fname_trans)[0]
# Plot the result in 3D brain
dip.plot_locations(fname_trans, 'sample', subjects_dir)
###############################################################################
# Calculate and visualise magnetic field predicted by dipole with maximum GOF
# and compare to the measured data, highlighting the ipsilateral (right) source
fwd, stc = make_forward_dipole(dip, fname_bem, evoked.info, fname_trans)
pred_evoked = simulate_evoked(fwd, stc, evoked.info, None, snr=np.inf)
# find time point with highes GOF to plot
best_idx = np.argmax(dip.gof)
best_time = dip.times[best_idx]
# rememeber to create a subplot for the colorbar
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=[10., 3.4])
vmin, vmax = -400, 400 # make sure each plot has same colour range
# first plot the topography at the time of the best fitting (single) dipole
plot_params = dict(times=best_time,
ch_type='mag',
outlines='skirt',
colorbar=False)
evoked.plot_topomap(time_format='Measured field', axes=axes[0], **plot_params)
dip = mne.fit_dipole(evoked, cov, bem, trans)[0]
# Plot the result in 3D brain with the MRI image.
dip.plot_locations(trans, mri_partic, subjects_dir=mri_dir, mode='orthoview')
#plot on scan
mni_pos = mne.head_to_mni(dip.pos, mri_head_t=trans,subject=mri_partic, subjects_dir=mri_dir)
mri_pos = mne.head_to_mri(dip.pos, mri_head_t=trans,subject=mri_partic, subjects_dir=mri_dir)
t1_fname = mri_dir+'\\'+ mri_partic+ '\\mri\\T1.mgz'
fig_T1 = plot_anat(t1_fname, cut_coords=mri_pos[0], title='Dipole loc.')
# #plot on standard
# from nilearn.datasets import load_mni152_template
# template = load_mni152_template()
# fig_template = plot_anat(template, cut_coords=mni_pos[0],title='Dipole loc. (MNI Space)')
#plot fied predicted by dipole with max goodness of fit, compare to data and take diff
fwd_dip, stc_dip = make_forward_dipole(dip, bem, evoked.info, trans)
pred_evoked = simulate_evoked(fwd_dip, stc_dip, evoked.info, cov=None, nave=np.inf)
# find time point with highest goodness of fit (gof)
best_idx = np.argmax(dip.gof)
best_time = dip.times[best_idx]
print('Highest GOF %0.1f%% at t=%0.1f ms with confidence volume %0.1f cm^3'
% (dip.gof[best_idx], best_time * 1000,
dip.conf['vol'][best_idx] * 100 ** 3))
# plot
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=[10., 3.4],
gridspec_kw=dict(width_ratios=[1, 1, 1],
top=0.85))
vmin, vmax = -400, 400 # make sure each plot has same colour range
