[Mne_analysis] single trial dSPM plots

[Matt Erhart]

It would be great to get a inverse epochs example that reflects the current
thinking in single trial localization, especially one that shows the best
way organize, scale, and display the single trials. Ultimately, I just want
to do some stats between conditions for single subjects or to plot sem
around an average condition waveform for a subject, so including that kind
of thing would be even better.


On Wed, Apr 23, 2014 at 11:30 AM, Tal Linzen <tal.linzen at gmail.com> wrote:

> 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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