Lambda2=lambda2, method=method, pick_ori=None)įor more information on this topic, I highly suggest you read through our The role of dipole orientations in distributed source localization tutorial. Stc = apply_inverse(evoked=evoked, inverse_operator=inverse_operator, Inverse_operator = make_inverse_operator(, fwd, noise_cov, Noise_cov = mne.compute_covariance(epochs, tmax=0., method='shrunk',įname_fwd = data_path / 'MEG' / 'sample' / 'sample_audvis-meg-oct-6-fwd.fif'įwd = mne.read_forward_solution(fname_fwd)Īx.plot(1e3 * stc.times, )Īx.set(xlabel='time (ms)', ylabel=f'', fontweight='bold')įig, axes = plt.subplots(2, 1, figsize=(6, 6), sharex=True, sharey=True)įor ax, fixed in zip(axes, (True, False)): Picks=('meg', 'eog'), baseline=baseline, reject=reject) Raw = mne.io.read_raw_fif(raw_fname) # already has an average referenceĮvents = mne.find_events(raw, stim_channel='STI 014') Raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_filt-0-40_raw.fif' Let’s see what this fixed parameter actually does: # %%įrom mne.minimum_norm import make_inverse_operator, apply_inverse In the Decoding (MVPA) tutorial, on the other hand, the following parameters are used: inv = mne.minimum_norm.make_inverse_operator(Ī value of loose=0 causes another parameter to be set automatically – fixed.In the Source localization with MNE/dSPM/sLORETA/eLORETA example, the following parameters are used: inverse_operator = make_inverse_operator(Į, fwd, noise_cov, loose=0.2, depth=0.8.The difference here is due to different parameters used when creating the inverse operator. When you do source localization in dSPM (see Source localization with MNE/dSPM/sLORETA/eLORETA - MNE 0.23.0 documentation ), you do not get any of the negative values, and the source model only shows the positive ones, but why does it behave differently in source modeling MVPA and shows both the negatives and the positives?.You will see that the blues do not have negative values in the temporal generalization matrix, but they do have negative values in the source modeled MVPA, but Why?Īlso, what exactly does this temporal generalization matrix show in the link above? When you do source localization in dSPM (see Source localization with MNE/dSPM/sLORETA/eLORETA - MNE 0.23.0 documentation), you do not get any of the negative values, and the source model only shows the positive ones, but why does it behave differently in source modeling MVPA and shows both the negatives and the positives?ĭoes the blue in MVPA source localization show that the activation is less than the computed mean? How about the red (activation above the computed mean)?ĭecoding (MVPA) - MNE 0.23.0 documentation.Regarding the second question that I asked earlier, yes the red shows values greater than 0, and blue shows values less than 0 in the source modeled MVPA. I love your MNE tutorials on YouTube! Great Job!Īnd sorry for my late reply! I did not receive any notification regarding your response to my question, and I only got it today.
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