[Mne_analysis] single trial dSPM plots

[Alexandre Gramfort]

hi Matt,

the problem is that you z-score at the single trial level.
You should apply the same z-score scaling based on
pooled variance from all trials

makes sense?

Alex


On Tue, Apr 22, 2014 at 12:53 AM, Matt Erhart <mattjerhart at gmail.com> wrote:
> Will give empty room a try. For ncov, I'm currently using:
>
> noise_cov = mne.compute_covariance(epochs_ica.crop(None, 0, copy=True))
>
> noise_cov = mne.cov.regularize(noise_cov, epochs_ica.info,mag=0.05,
> grad=0.05,eeg=0.1, proj=True)
>
>
> thanks,
>
> Matt
>
>
>
> On Mon, Apr 21, 2014 at 12:21 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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