[Mne_analysis] noise covariance matrix

[Alexandre Gramfort]

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.
>
>
>
>
>
>
>
> *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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