# 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