[Mne_analysis] equivalence of averaged single trial STCs and evoked STCs

[Denis-Alexander Engemann]

Hi Hari,

the 'nave' parameter is supposed to be 'n_epochs' in the evoked case and 1
in the single trial case.

It seems, post-hoc normalizing to the baseline does the trick, as suspected:

https://www.dropbox.com/s/kugvns9klfqhu2z/methods_comparison_evoked_single_trial.pdf

once more, gist updated: https://gist.github.com/dengemann/9470121

What do people think?

Best,
Denis



On Mon, Mar 10, 2014 at 8:42 PM, Hari Bharadwaj <hari at nmr.mgh.harvard.edu>wrote:

> Also, it might be interesting to see what the default 'nave's are
> apply_inverse() and apply_inverse_epochs() and what implications that
> might have..
>
>
> On Mon, March 10, 2014 3:26 pm, Denis-Alexander Engemann wrote:
> > [reposted, attachment failure]
> >
> > Hi Dan,
> >
> > that was indeed a very good hint.
> >
> > I've updated the gist to do some more systematic comparisons between
> > orientations and methods:
> >
> > https://gist.github.com/dengemann/9470121
> >
> > As to you clarification questions, the lines referred to in the legend
>  as
> > 'single*' are related to averaging in source space.
> >
> > It seems using MNE and with a post-hoc normalization, e.g. using z-scores
> > might be the way to go.
> >>From my ad-hoc parameter experiment I cannot exclude the possibility,
> >> that
> > 'dSPM' and 'sLORETA' with 'normal' orientation might work as well.
> > This would require a mean-flip method though which is not implemented in
> > my
> > script due to the manual extraction used.
> > My extraction function, here, was `lambda x: np.abs(x).mean(0)`
> >
> > This needs more investigation + discussion
> >
> > Denis
> >
> >
> > Images:
> >
> > https://www.dropbox.com/s/e4ykrm6xdh6ntio/fig-None-MNE.png
> >
> > https://www.dropbox.com/s/qwlt5ugxxo0owo2/fig-normal-MNE.png
> >
> > https://www.dropbox.com/s/mijmz9pvffgt5ms/fig-None-sLORETA.png
> >
> > https://www.dropbox.com/s/8dxf0czptuhyybs/fig-normal-sLORETA.png
> >
> > https://www.dropbox.com/s/hxcjla8x9euwzd3/fig-None-dSPM.png
> >
> > https://www.dropbox.com/s/e5pbr7iyday3za4/fig-normal-dSPM.png
> >
> >
> >
> >
> > On Mon, Mar 10, 2014 at 7:13 PM, dgw <dgwakeman at gmail.com> wrote:
> >
> >> Hi Denis,
> >>
> >> Which line represents "averaging in source space" and which represents
> >> "projecting evokeds"?
> >>
> >> Regardless. I think you are referring to why are the results different
> >> when I average single trial data on the source space as opposed to
> >> averaging that same data in sensor space and then source localizing?
> >>
> >> The first reason I can think of is that you are using the dSPM here and
> >> I
> >> believe that that can lead to some non-linearity between these results.
> >> Do
> >> you get the same problem with the L2?
> >>
> >>  HTH,
> >> D
> >>
> >>
> >> On Mon, Mar 10, 2014 at 1:56 PM, Denis-Alexander Engemann <
> >> denis.engemann at gmail.com> wrote:
> >>
> >>> Hi folks,
> >>>
> >>> in one of my recent analyses I ran into some problems with regard to
> >>> morphing and single trial
> >>> label time series extraction which lead me to set up some comparisons:
> >>>
> >>> https://gist.github.com/dengemann/9470121
> >>>
> >>> (gist based on sample data, should run with a proper MNE-Python
> >>> install)
> >>>
> >>> [image: Inline image 1]
> >>>
> >>> It turned out morphing was unproblematic, but,
> >>> I'm wondering whether the differences between averaging in source space
> >>> and
> >>> projecting evokeds is expected, and if so, how it can be avoided?
> >>>
> >>> The background is that I'd like to be confident about the relating
> >>> single
> >>> trial analyses at the evoked level.
> >>>
> >>> Any thoughts?
> >>>
> >>> Cheers,
> >>> Denis
> >>>
> >>>
> >>>
> >>>
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>
> --
> Hari Bharadwaj
> PhD Candidate, Biomedical Engineering,
> Boston University
> 677 Beacon St.,
> Boston, MA 02215
>
> Martinos Center for Biomedical Imaging,
> Massachusetts General Hospital
> 149 Thirteenth Street,
> Charlestown, MA 02129
>
> hari at nmr.mgh.harvard.edu
> Ph: 734-883-5954
>
>
>
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