[Mne_analysis] covariance with 'auto' option

[Elena Orekhova]

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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