[Mne_analysis] noise covariance matrix

Sat Apr 8 10:09:19 EDT 2017

Hi Lucy,

your plots show that you're using SSS. We recently saw some rank estimation
issues in some case at the level of the covariance matrix but not yet fully
understand the situation.
Can I ask you to plot the covariance as follows:

```
cov.plot(evoked.info)
```

then you could try to plot the whitening again, this time using our non
public maintenance function.

```
meg_picks = mne.pick_types(meg=True, eeg=False)
sss_rank = raw.estimate_rank(picks=meg_picks)
mne.viz.evoked._plot_white(evoked, noise_cov, rank=sss_rank)
```

As a sanity check, the ```sss_rank``` should correspond to the prominent
kink in your eigenvalue spectrum of the covariance.

If the display improves your file also belongs to the mysterious group of
SSS'ed files for which our defaults aren't optimized.

I'm currently working on an improvement of our covariance computation with
regard to the data rank. If you are happy to share an epochs or raw file
with me privately I could use that as another test file for development.

Best,
Denis

On Fri, Apr 7, 2017 at 4:33 AM Lucy MacGregor <
Lucy.MacGregor at mrc-cbu.cam.ac.uk> wrote:

> Hi Alex,
>
>
>
> Many thanks for the suggestion.
>
>
>
> I have tried again, now with a HPF of 1Hz (previously was 0.1Hz). The EEG
> look better (although I realise I have some noisy channels in there). For
> the MEG the GFP looks much better for empirical, with values around 1.
> However, for ‘shrunk’ the values are below 1. But ‘shrunk’ is chosen as the
> best method.
>
>
>
> Please do you have an explanation for why the GPF<1 for the ‘shrunk’
> method?
>
> Do you have a suggestion as to what I could do (e.g. just choose
> ‘empirical’ or make some other changes)?
>
>
>
> [image: image004.jpg]
>
>
> Many thanks for your advice.
>
>
>
> Best wishes,
>
>
>
> Lucy
>
>
>
> *From:* mne_analysis-bounces at nmr.mgh.harvard.edu [mailto:
> mne_analysis-bounces at nmr.mgh.harvard.edu] *On Behalf Of *Alexandre
> Gramfort
> *Sent:* 06 April 2017 14:35
> *To:* Discussion and support forum for the users of MNE Software
> *Subject:* Re: [Mne_analysis] noise covariance matrix
>
>
>
> Hi Lucy,
>
>
>
> you seem to have some channels with very big drifts. That's why you see
>
> so huge GFP values. If it's acceptable for your type of question you could
>
> high pass a bit to fix this.
>
>
>
> HTH
>
> Alex
>
>
>
> On Thu, Apr 6, 2017 at 6:29 AM, Lucy MacGregor <
> Lucy.MacGregor at mrc-cbu.cam.ac.uk> wrote:
>
>
>
>
>
> Dear MNE users,
>
>
>
> I would very much appreciate your advice on the results I am getting from
> calculation of the noise covariance matrix. I’m using the “method” option
> for mne.compute_covariance to do automated regularisation.
>
>
>
> Data were collected with Neuromag 306 Vectorview system. My responses are
> time locked to the onset of the average of ~300 auditorily-presented
> sentences. I have used the silent (baseline) period -500-0ms before
> sentence-onset as the time period from which to estimate the noise.
>
>
>
> ##################
>
> event_id = None
>
> tmin, tmax = -0.5, 5.5
>
> reject_tmin, reject_tmax = -0.5, 1.5
>
> bmin, bmax = -0.5, 0
>
>
>
> epochs = mne.Epochs(raw, events, event_id, tmin, tmax, reject_tmin =
> reject_tmin, reject_tmax = reject_tmax, picks=picks, baseline=baseline,
> reject=reject, preload=True, add_eeg_ref=True)
>
> noise_cov = mne.compute_covariance(epochs, method =(‘shrunk’,
> ‘empirical’), tmin=bmin, tmax=bmax, return_estimators = True)
>
> ###################
>
>
>
> The plot below is for a single subject (but all my subjects show
> similar-looking output) for a period -500 to 5000ms covering the duration
> of my sentences.
>
>
>
> I have compared my output with that for the examples:
>
>
> http://martinos.org/mne/stable/auto_examples/visualization/plot_evoked_whitening.html#sphx-glr-auto-examples-visualization-plot-evoked-whitening-py
>
> http://martinos.org/mne/stable/auto_tutorials/plot_compute_covariance.html
>
>
>
> The result tells me that “shrunk” is the best method, but from looking at
> the output from whitening I’m unsure how this is the case, and in fact
> whether either method is working as it should.
>
>
>
> [image: image003.jpg]
>
>
>
>
>
> *Evoked signals for all channels:*
>
> For the MEG, during the baseline the values are generally within the +/-
> 1.96 indicated by the red dotted line, so I think this is OK.
>
> Data look quite noisy
>
>
>
> *GFP plots for MEG:*
>
> For ‘empirical’, the baseline values > 1 whereas for ‘shrunk, the baseline
> values <1. As I understand it values should be around 1 and therefore both
> methods look problematic.
>
>
>
> My question is therefore:
>
> *when the baseline GFP is > or < 1 then is this due to problems with
> regularisation and where should I go from here?*
>
>
>
>
>
> With thanks for your thoughts and advice.
>
>
>
> Kind regards,
>
>
>
> Lucy MacGregor
>
>
>
>
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