[Mne_analysis] Regularization of the noise covariance matrix with tSSSed data

[Sheraz Khan]

hi Ying,

Rey might pinch in more, my few cents, tsss projection are not orthgonal in
space, so they do not reduce the rank as SSS or PCA, tsss projections
reduces the variance of the first few components, since most rank
determining algoritham uses some sort of ratio of highest to lowest, after
tsss if you try to determine rank in Matlab or python they might give you a
value higher than 64.

Best rank guest should be 64 - all the PCA projection you are using.

sheraz
On Jul 18, 2014 7:52 AM, "Alexandre Gramfort" <
alexandre.gramfort at telecom-paristech.fr> wrote:

> hi Ying,
>
> try at first without regularizing. MNE should detect the rank
> automatically when whitening. It's however possible that tSSS makes
> rank estimation difficult due to slight head positions changes.
>
> maybe Sheraz or Rey can share their insights too.
>
> Alex
>
> On Thu, Jul 17, 2014 at 6:43 PM, Ying Yang
> <evergreenlifeyangying at gmail.com> wrote:
> > Hi MNE experts,
> >
> > I am wondering whether it is common to regularize the noise covariance
> > matrix for tSSSed data. Since tSSS may reduce the rank of the sensor
> data,
> > (in my case to 64),  the covariance matrix is singular too.  As a result,
> > the whitened forward gain matrix had a lot of zeros.
> >  In this case, is it better to regularize the noise covariance matrix,
> or to
> > leave it intact such that the rank of noise matches the rank of
>  "signal"?
> > Thanks in advance!
> >
> > Best,
> > Ying
> >
> >
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