[Mne_analysis] covariance with 'auto' option

Denis-Alexander Engemann denis.engemann at gmail.com
Tue Oct 18 12:01:26 EDT 2016
Search archives:

Hum this is interesting. I'm not sure what is making the shrunk cov look
underfitting here. On the butterfly it looks good on top of that. I would
need to play with the data to better understand what maybe going on but I
have little time at this point. It seems anyways that you have good amounts
of data and the regularized covariance is not so different from the
empirical one. As long as the butterfly plot looks ok you should be safe
and trust the result that is based on the negative loglikelihood. Out of
curiosity, how many trials do you have, what is the sampling frequency and
did you check with other recordings?

I hope this helps,
Denis


On Tue, Oct 18, 2016 at 1:10 PM Elena Orekhova <orekhova.elena.v at gmail.com>
wrote:

> >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
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>
>
> The information in this e-mail is intended only for the person to whom it
> is
> addressed. If you believe this e-mail was sent to you in error and the
> e-mail
> contains patient information, please contact the Partners Compliance
> HelpLine at
> http://www.partners.org/complianceline . If the e-mail was sent to you in
> error
> but does not contain patient information, please contact the sender and
> properly
> dispose of the e-mail.
>
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>
>
> The information in this e-mail is intended only for the person to whom it
> is
> addressed. If you believe this e-mail was sent to you in error and the
> e-mail
> contains patient information, please contact the Partners Compliance
> HelpLine at
> http://www.partners.org/complianceline . If the e-mail was sent to you in
> error
> but does not contain patient information, please contact the sender and
> properly
> dispose of the e-mail.
>
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>
>
> The information in this e-mail is intended only for the person to whom it
> is
> addressed. If you believe this e-mail was sent to you in error and the
> e-mail
> contains patient information, please contact the Partners Compliance
> HelpLine at
> http://www.partners.org/complianceline . If the e-mail was sent to you in
> error
> but does not contain patient information, please contact the sender and
> properly
> dispose of the e-mail.
>
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>
>
> The information in this e-mail is intended only for the person to whom it
> is
> addressed. If you believe this e-mail was sent to you in error and the
> e-mail
> contains patient information, please contact the Partners Compliance
> HelpLine at
> http://www.partners.org/complianceline . If the e-mail was sent to you in
> error
> but does not contain patient information, please contact the sender and
> properly
> dispose of the e-mail.
>
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>
>
> The information in this e-mail is intended only for the person to whom it
> is
> addressed. If you believe this e-mail was sent to you in error and the
> e-mail
> contains patient information, please contact the Partners Compliance
> HelpLine at
> http://www.partners.org/complianceline . If the e-mail was sent to you in
> error
> but does not contain patient information, please contact the sender and
> properly
> dispose of the e-mail.
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20161018/437edd3b/attachment-0001.html 


More information about the Mne_analysis mailing list