[Mne_analysis] Noise covariance matrix

[Laura Munkki]

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Hi Denis,
Thank you very much for your answer,
The last question: is it *acceptable* to use the NCM from unfiltered epochs
(after the maxfiltering and ICA) for modelling ERPs which were filtered
after averaging, or it would be totally wrong?
Best,
Laura


On Thu, Mar 26, 2020 at 7:10 PM Denis A. Engemann <
denis-alexander.engemann at inria.fr> wrote:

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>
> Hi Laura,
>
> Regarding 1) I would see no a priori reason for filtering once more, also
> filtering is more accurate on the long raw time series. Filtering at later
> stages may be helpful for exploring ideas but if you can avoid that’s
> better.
> Regarding 2) It depends if you at least apply maxfiler & ICA to the  data.
> If you don’t temporally filter, your noise-covariance will be more strongly
> influence by low frequencies, e.g., environmental noise, if you filter, it
> may help suppressing spatial patterns due to background brain activity e.g.
> alpha band, hence, yield enhance SNR for your activity of interest.
> If you don’t filter, it will give a solution that looks more like one that
> is based on the noise covariance from empty room. But applying the same
> preprocessing in terms of SSP/SSS/ICA is important in any case.
> If it helps, think that the noise covariance defines a noise model for
> MNE/dSPM.
> The inverse solution will be relative to that model.
> If you have a doubt about the content of your covariance, see the plotting
> trick in this tutorial
> https://mne.tools/dev/auto_examples/inverse/plot_mne_cov_power.html?highlight=apply%20inverse%20cov
> to visualize the diagonal of the  covariance as topomap. It can  give you a
> feeling for whether you capture brain sources in your covariance.
>
> Hope that helps,
> Denis
>
> > On Mar 26, 2020, at 4:43 PM, Laura Munkki <lauramunkki at gmail.com> wrote:
> >
> >         External Email - Use Caution
> >
> >
> > Hi Denis,
> > 1. If the raw data is filtered before the ICA, should one filter
> averaged responses again before source modelling?
> > 2. In case of the situation I described above, is it possible to use NCM
> calculated for unfiltered NCM-epochs, or it is totally wrong?
> > Thank you again!
> >
> >
> > On Thu, Mar 26, 2020 at 5:19 PM Denis A. Engemann <
> denis-alexander.engemann at inria.fr> wrote:
> >         External Email - Use Caution
> >
> > Hi Laura,
> >
> > To me the safest thing is bandpass filtering at raw stage, e.g., prior
> to ICA.
> > Then you keep processing identical for data and noise covariance and
> just use the baseline segments from the otherwise  identically processed
> epochs.
> > Have you tried that?
> >
> > Denis
> >
> > > On Mar 26, 2020, at 4:11 PM, Laura Munkki <lauramunkki at gmail.com>
> wrote:
> > >
> > >         External Email - Use Caution
> > >
> > >
> > > Hi Denis,
> > > Thank you very much for your reply.
> > > I did maxfiltering and ICA for the raw data, so these steps are
> applied for both types of epochs, evoked responses and NCM. But I did not
> use any band-pass filters before averaging. I filtered only averaged ERPs
> just before source reconstruction. So, if I filter NCM epochs, it will not
> be the same. My question is: when should I apply a band-pass filter for the
> NCM? I tried to apply it to the NCM-epochs file, but the result was
> distorted: instead of sources around auditory cortex I've got several
> sources in unpredictable locations. But when I tried to use NCM calculated
> for unfiltered NCM-epochs, the sources were around auditory cortex and had
> much stronger amplitude.
> > > Looking forward for your answer,
> > > Thank you very much for helping me,
> > > Laura.
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