[Mne_analysis] covariance with 'auto' option

[Denis-Alexander Engemann]

That looks already much better :)
The whitened butterfly plot on the other hand looks good.
How does this butterfly plot look like for the empirical covariance?
It's generally not always easy to understand what is behind such scaling
issues as this diagnostic plot is very sensitive to subtle model
violations. That shrunk is selected means that it has for mathematical
reasons the better properties as an estimator of the covariance of unseen
data, to be preferred over the plot in case of doubt. For the GFP plot
subtle differences in rank estimates can also lead to wrong scaling. Is
your actual number of SSS components 74 as displayed in the plot?

Denis

On Tue, Oct 18, 2016 at 11:10 AM Elena Orekhova <orekhova.elena.v at gmail.com>
wrote:

> Thank you Denis, it was helpful!
>
> I tried it with
>
> cov = mne.compute_covariance(allepochs, method=['empirical', 'shrunk'] ,
> tmin=-0.8, tmax=0.0 , return_estimators=True, verbose=True)
>
>
>
> The best was the ‘shrunk’.  However, the GFP is lower then 1 for the
> ‘shrunk’ and is close to 1 for ‘empirical’ (see the figure). Is it OK?
>
>
>
> Elena
>
> On 18 October 2016 at 10:31, Denis-Alexander Engemann <
> denis.engemann at gmail.com> wrote:
>
> Hi Elena,
>
> It looks like you are the entire time window? If you do this because you
> have cropped epochs for the purpose of cov estimation then the idea of this
> plot is to inspect the data segments that were not part of the window used
> for cov estimation.
> The plot further suggest that you are using data processed with SSS which
> are rank deficient. Factor Analysis is not expected to work well. For SSS
> the "shrunk" option should do a good job. I would run it with
> method=('empirical', 'shrunk') and return the estimators (see parameter) to
> compare them. One should always compare the fancier estimators with the
> empirical covariance. In that case you would pass a list of covariance
> objects to the plot_white method which will then show you one time series
> per covariance.
>
> I hope this helps,
> Denis
>
>
> On Tue, Oct 18, 2016 at 10:22 AM Elena Orekhova <
> orekhova.elena.v at gmail.com> wrote:
>
> Hello,
>
> I calculated noise covariance matrix on baseline using method=’auto’ to
> find an optimal regularization:
>
> cov = mne.compute_covariance(covepochs, method= ’auto’ , tmin=None,
> tmax=None , verbose=True)
>
>
>
> The optimal was ‘factor analysis’, but it gave me unacceptable solution.
>
> When I look at whitening, it seems that ‘shrunk’ works better (see the
> figures attached)!  What can be the problem?
>
>
>
>
>
>  Elena
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