[Mne_analysis] compute_covariance estimations for different participants

van Bijnen, Sam sam.s.vanbijnen at jyu.fi
Thu Nov 29 03:56:33 EST 2018
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Hi Denis,


Thank you for your response, I should probably mention it is quite a long recording (~800-900 epochs) and the data is already maxfiltered (tSSS), also the participants were mostly children so noise varies over time and between participants quite a lot. Until now I have been using the shrunk method for all participants, do you think it is worth it to switch to the algorithm with the methods you mentioned? From your article(s) about the cross-validation, I gathered that shrunk and FA methods were the best to pick. I tried SC and FA on a few participants and FA seemed to do slightly better, the thing is I would need to rerun the forward and inverse model for about 100 participants. If it should increase the comparability in a meaningful way I would, of course, do it.


I appreciate the help!


Best,

Sam

________________________________
From: mne_analysis-bounces at nmr.mgh.harvard.edu <mne_analysis-bounces at nmr.mgh.harvard.edu> on behalf of Dr. Denis-Alexander Engemann <denis-alexander.engemann at inria.fr>
Sent: Wednesday, November 28, 2018 3:54:02 PM
To: Discussion and support forum for the users of MNE Software
Subject: Re: [Mne_analysis] compute_covariance estimations for different participants

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Hi Sam,

The differences between the methods are rather subtle when you have long recordings.
I’d recommend to do at least methods=[‘empirical’, ’shrunk’] or [‘empirical’, ‘oas’] as a quick compromise and the way to go when using maxfilter for cleaning the MEG. In most cases, the regularized one will win.
Letting the algorithm decide how to regularize is better than forcing one method to all participants and should make your data more comparable, not less comparable.

Hope that helps,
Denis

> On Nov 28, 2018, at 2:41 PM, van Bijnen, Sam <sam.s.vanbijnen at jyu.fi> wrote:
>
>         External Email - Use Caution
> Hi,
>
> When using compute_covariance for combined M/EEG data, is it advisable to use the same method for all participants, or should I let the cross-validation procedure pick the best method for each participant?
>
> Thank you!
> Sam
>
>
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