[Mne_analysis] single trial dSPM plots

[Tal Linzen]

I feel like the recommendation to use MNE instead of dSPM in single-trial
source solutions has come up on the mailing list more than once, I think,
but the example code distributed with MNE Python still uses dSPM:

http://martinos.org/mne/stable/auto_examples/inverse/plot_compute_mne_inverse_epochs_in_label.html


On Mon, Apr 21, 2014 at 3:21 PM, dgw <dgwakeman at gmail.com> wrote:

> Hi Matt,
>
> I am unsure if this is a scaling problem. Remember the dSPM is
> essentially an F test against the noise. The brain is very busy all
> the time, so your SNR is pretty low, because you are only interested
> in the activity relative to your task, while all that other brain
> activity is going on in the single trial data. Averaging dramatically
> improves the SNR.
>
> Short version: If you are use the dSPM, I expect the single trial to
> look very poor (especially if you are using prestimulus data for the
> noise covariance matrix). It may make more sense to look at single
> trial data using the MNE. And if you really must use single trial data
> with a dSPM, I recommend using emptyroom data (if this is MEG) instead
> of prestimulus data for your noise covariance matrix.
>
> I don't think it would be a problem for a figure to show the average
> dSPM and the single trial MNE (with two y axes: the left with the dSPM
> score and the right with the MNE amplitudes for the single trial
> data).
>
> HTH
> D
>
> On Mon, Apr 21, 2014 at 3:08 PM, Matt Erhart <merhart at ucsd.edu> wrote:
> > How should I scale single trial dspm timecourses (from a label) so they
> can
> > be plotted together with the average across trials? Currently, my average
> > across trials looks good, but the single trials don't seem to match the
> > average, so I assume I am scaling the single trials wrong. Here's the
> > plotting code snippet:
> >
> > ...
> > #left/right tones
> > stcs_RL = apply_inverse_epochs(epochs_ica['RL'], inverse_operator,
> lambda2,
> > method,
> >                             pick_ori="normal")
> >
> > #https://gist.github.com/dengemann/9470121
> > times = epochs_ica.times * 1e3
> > def xfun(x, times):
> >     x = np.abs(x).mean(0)
> >     baseline = times < 0
> >     x -= x[baseline].mean(0)[None]
> >     x /= x[baseline].std(0)[None]
> >     return x
> >
> > mean_stc2 = sum(stcs_LR[:])
> > mean_stc2._data /= len(stcs_LR[:])
> >
> > for i in range(np.shape(stcs_LR)[0]):
> >     time_course2 = xfun(stcs_LR[i].in_label(label).data, times)
> >     plt.plot(times, time_course2)
> >     plt.xlabel('Time (ms)')
> >
> > mean_timecourse = xfun(mean_stc2.in_label(label).data, times)
> > plt.plot(times,mean_timecourse.T,linewidth=5)
> >
> > Here's a image of the single trials under the average across trials. They
> > don't seem to match up but the average is what I would expect.
> >
> > If there was a gist around somewhere that shows how to plot single trials
> > from a label and the average together correctly, that'd be great.
> >
> > thanks,
> > Matt
> >
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