[Mne_analysis] single-trial mne sources

[MARTI Sébastien INSERM]

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Dear MNE users,

We are trying to perform source localization at the single trial. For a reason we don't understand, the results we obtain when applying the inverse operator on each trial and then computing the average across trials do not really make sense when compared to applying the inverse operator to the averaged epochs. In other words, applying the inverse operator on the evoked object doesn't seem to be equivalent to averaging across trials the sources from each trial. We expected the two to be equivalent since the inverse operation is linear, but perhaps due to regularization, or another reason, the two shouldn't be the same.



If any of you had an example script for source localization at the single trial level it would help us to understand what we are doing wrong.

We have also looked at this example on mne website: "Compute MNE-dSPM inverse solution on single epochs", but didn't find the solution there.



Many thanks in advance



Best



Sébastien & Yair





Some details:

mne version: 0.16.dev0



OS: Ubuntu 16.04



Code:





        '''

        apply inverse operator and compare conditions

        '''



        import sys

        import os

        from os.path import join



        import mne

        from mne import morph_data

        from mne.source_space import (setup_source_space, morph_source_spaces,

add_source_space_distances)

        from mne.minimum_norm import (make_inverse_operator, read_inverse_operator, apply_inverse_epochs)

        from sentcomp_epoching import get_condition

        import numpy as np

        import numpy.matlib



        data_path = '/neurospin/meg/meg_tmp/sentcomp_Marti_2016/Seb/data'

        subjects_dir = join(data_path, 'anatomy', 'subjects')

        meg_dir = join(data_path, 'sss')



        os.environ['SUBJECTS_DIR'] = subjects_dir

        os.environ['MEG_DIR'] = meg_dir



        speed_oi = 4

        if speed_oi==1:

            real_speed = .083

        elif speed_oi==2:

            real_speed = .116

        elif speed_oi==3:

            real_speed = .166

        elif speed_oi==4:

            real_speed = .301



        # read epochs

        epochs = mne.read_epochs(join(data_path, 'epochs',  subject + '_speed'

+ str(speed) + '_V2-epo.fif'))

        epochs.apply_baseline(baseline=(-0.76,-0.3), verbose=None)



        # read inverse operator

        inv_meg = read_inverse_operator(join(data_path, 'sources', subject +

'-meg-oct-6-meg-inv.fif'))



        # get conditions

        knames2, ep2 = get_condition(conditions=c2,epochs=epochs,startTime=-.2,

duration=1.5,real_speed=real_speed)

        if timelock_to_word==True:

                ev2 = ep2.average()

        else:

                ev2 = epochs[knames2].average()

                ep2 = epochs[knames2]



        snr = 3.0

        lambda2 = 1./snr**2



        # apply inverse operator

        stc_meg2 = apply_inverse_epochs(ep2, inv_meg, lambda2=lambda2, method='MNE', pick_ori=None, nave=1)

        av_stc_meg2 = np.mean(stc_meg2) # average of inverse



         ev_stc_meg2 = apply_inverse(ev2, inv_meg, lambda2=lambda2, method='MNE', pick_ori=None) # inverse of average



         av_stc_meg2.plot(subject='am150105', surface='inflated', hemi='lh', smoothing_steps=12, time_viewer=True, subjects_dir=subjects_dir, figure=None, views='lat', backend='mayavi', time_unit='ms', spacing='oct6')

         ev_stc_meg2.plot(subject='am150105', surface='inflated', hemi='lh', smoothing_steps=12, time_viewer=True, subjects_dir=subjects_dir, figure=None, views='lat', backend='mayavi', time_unit='ms', spacing='oct6')




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