[Mne_analysis] single trial dSPM plots

[Hari Bharadwaj]

Hi Matt,
   Please have a try with this:
https://gist.github.com/haribharadwaj/11232865

Here, the single trial dSPM and the evoked dSPM are shown from the same
label (pooled across vertices of the label).. I have used the same inverse
operator for the two (i.e., scaled the noise-cov identically).. Not sure
if this is representative of "current thinking" but nonetheless something
I look at for exploratory purposes when I want to view single trials..

Hari


On Wed, April 23, 2014 3:11 pm, Matt Erhart wrote:
> 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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>>
>>
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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