[Mne_analysis] covariance with 'auto' option

Elena Orekhova orekhova.elena.v at gmail.com
Tue Oct 18 07:10:29 EDT 2016
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>How does this butterfly plot look like for the empirical covariance?

It looks pretty much the same as the 'shrunk' (attached).



> Is your actual number of SSS components 74 as displayed in the plot?

If I do

rank = raw.estimate_rank(tstart=0.0, tstop=None, tol=0.0001,
return_singular=False, picks=None, scalings='norm')

rank =74


Elena

On 18 October 2016 at 11:44, Denis-Alexander Engemann <
denis.engemann at gmail.com> wrote:

> 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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