[Mne_analysis] Noise covariance on Neuromag combined M/EEG

[Denis-Alexander Engemann]

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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