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

[Ying Yang]

Thanks a lot for all the answers!
Ying


On Fri, Jul 18, 2014 at 10:56 AM, Sheraz Khan <sheraz at nmr.mgh.harvard.edu>
wrote:

> 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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