[Mne_analysis] Noise covariance on Neuromag combined M/EEG

Fri Dec 4 04:25:11 EST 2015

Hi Denis,

and thanks for the quick reply. This helped a lot. And sound great, the toolkit is advancing fast. The warnings given in the article about rank deficient data seemed so ominous. I'm going to give this a try once all the preprocessing is done.

Best,
Tatu

On Thu, 3 Dec 2015 12:00:02 -0500
<mne_analysis-request at nmr.mgh.harvard.edu> wrote:

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>    1. Noise covariance on Neuromag combined M/EEG (Huovilainen, Tatu M)
>    2. Re: Noise covariance on Neuromag combined M/EEG
>       (Denis-Alexander Engemann)
> 
> 
> ----------------------------------------------------------------------
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> Message: 1
> Date: Thu, 3 Dec 2015 15:56:15 +0000
> From: "Huovilainen, Tatu M" <tatu.huovilainen at helsinki.fi>
> Subject: [Mne_analysis] Noise covariance on Neuromag combined M/EEG
> To: "mne_analysis at nmr.mgh.harvard.edu"
> 	<mne_analysis at nmr.mgh.harvard.edu>
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> 
> Hi all,
> 
> I'm working with combined M/EEG dataset measured with Neuromag and I'm wondering about the whitening step before ICA. Right now the MEG part of the data is tSSS'd and movement corrected (with cHPI to 'default head position') so the rank seems to end up being around 70. Engemann and Gramfort (2015, below) point out that with combined M/EEG a FA model should be used for the estimation as the noise levels between sensor types are heteroscedastic, but also recommend not to use FA model after the dimensionality has been reduced. How do you recommend I find the noise covariance matrix in my case?
> 
> Engemann and Gramfort recommend computing the FA model before the SSS and then applying dimensionality reducing operators to both the data and the covariance estimator. How would this work?
> 
> I'm not sure about tSSS, but I have to use at least the movement correction as I'm aiming for ICA decomposition and further analyses in the IC domain. I also have combined M/EEG "empty room" measurement, so a participant not doing anything for a few minutes.
> 
> Regards,
> Tatu
> 
> Engemann, D. A., & Gramfort, A. (2015). Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals. NeuroImage, 108, 328-342.
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> Message: 2
> Date: Thu, 3 Dec 2015 17:35:52 +0100
> From: Denis-Alexander Engemann <denis.engemann at gmail.com>
> Subject: Re: [Mne_analysis] Noise covariance on Neuromag combined
> 	M/EEG
> To: Discussion and support forum for the users of MNE Software
> 	<mne_analysis at nmr.mgh.harvard.edu>
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> 
> Hi Tatu,
> 
> let me respond to you inline,
> 
> On Thu, Dec 3, 2015 at 4:56 PM, Huovilainen, Tatu M <
> tatu.huovilainen at helsinki.fi> wrote:
> 
> > Hi all,
> >
> > I'm working with combined M/EEG dataset measured with Neuromag and I'm
> > wondering about the whitening step before ICA. Right now the MEG part of
> > the data is tSSS'd and movement corrected (with cHPI to 'default head
> > position') so the rank seems to end up being around 70.
> >
> That's ok just use <= this value for n_components.
> 
> 
> Engemann and Gramfort (2015, below) point out that with combined M/EEG a FA
> > model should be used for the estimation as the noise levels between sensor
> > types are heteroscedastic,
> >
> we went beyond that, the 'shrunk' estimator that you have as an option in
> MNE uses different regularizations for the sensor types. And the idea is
> still that you cover different potential scenarios by
> picking the best covariance estimator as measured by the negative
> loglikelihood on unseen data.
> 
> > but also recommend not to use FA model after the dimensionality has been
> > reduced. How do you recommend I find the noise covariance matrix in my case?
> >
> Engemann and Gramfort recommend computing the FA model before the SSS and
> > then applying dimensionality reducing operators to both the data and the
> > covariance estimator. How would this work?
> >
> you can try FA it's mostly a numerical problem, I think we have improved it
> up to a point where it can work even on SSSed data.
> 
> > I'm not sure about tSSS, but I have to use at least the movement
> > correction as I'm aiming for ICA decomposition and further analyses in the
> > IC domain. I also have combined M/EEG "empty room" measurement, so a
> > participant not doing anything for a few minutes.
> >
> On event-related data with a noise covariance from uninteresting data I
> usually first apply ICA to make sure the rank reduction is consistent. But
> we meanwhile improved our down-stream code that handles the numerical rank
> in the source-localization, it will probably be just fine.
> 
> Hope that helps,
> Denis
> 
> > Regards,
> > Tatu
> >
> > Engemann, D. A., & Gramfort, A. (2015). Automated model selection in
> > covariance estimation and spatial whitening of MEG and EEG signals.
> > NeuroImage, 108, 328-342.
> >
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-- 
Tatu Huovilainen <Tatu.Huovilainen at helsinki.fi>