[Mne_analysis] What is a "good" noise covariance matrix?

[dgw]

Noise Covariance Estimates should always be treated exactly the same as
your data:

You should always do a full visual inspection of the raw data used to
calculate your noise covariance.

All of the operators applied to your real data (e.g. filters and SSP
vectors etc.) should be applied to your noise covariance data as well.

HTH
D

On Wed, Oct 1, 2014 at 10:02 AM, Baptiste Gauthier <gauthierb.ens at gmail.com>
wrote:

> Dear mne-python experts and users,
>
> following the guidelines of source reconstruction of ERFs, I estimated
> noise covariance matrices from empty room noise (neuromag system) for
> calculating inverse operator. When looking at the source estimates I got,
> it appears that source amplitude can be very variable, not in term of
> timecourse patterns (which is good for ERFs) but in term of absolute
> amplitude (need to play with "fmult" in mne_analyze visualization tools; I
> suppose it's bad for stats).
> So I checked if the noise estimation was similar across subjects and
> realize I have no criterion to decide if noise covariance was "ok" or
> not...
> What criterion should I apply?
> Should I use then regularization for "bad" subjects?
>
> PS:find attached several noise covariance matrices from my study
> PPS: Does it make sense to band-pass the empty room signal with the same
> classical band pass applied to the data? Can it improve a bit the thing?
>
> Best,
>
> Baptiste Gauthier
>
>
>
>  bad?.png
> <https://docs.google.com/file/d/0B_eZxstAMJQscGpiOF9VY00yLWc/edit?usp=drive_web>
>
>  good?.png
> <https://docs.google.com/file/d/0B_eZxstAMJQsY01WdGlJbENHa0U/edit?usp=drive_web>
>
>
> 2014-10-01 14:05 GMT+02:00 Baptiste Gauthier <gauthierb.ens at gmail.com>:
>
>> Dear mne-python experts and users,
>>
>> following the guidelines of source reconstruction of ERFs, I estimated
>> noise covariance matrices from empty room noise (neuromag system) for
>> calculating inverse operator. When looking at the source estimates I got,
>> it appears that source amplitude can be very variable, not in term of
>> timecourse patterns (which is good for ERFs) but in term of absolute
>> amplitude (need to play with "fmult" in mne_analyze visualization tools; I
>> suppose it's bad for stats).
>> So I checked if the noise estimation was similar across subjects and
>> realize I have no criterion to decide if noise covariance was "ok" or
>> not...
>> What criterion should I apply?
>> Should I use then regularization for "bad" subjects?
>>
>> PS:find attached several noise covariance matrices from my study
>> PPS: Does it make sense to band-pass the empty room signal with the same
>> classical band pass applied to the data? Can it improve a bit the thing?
>>
>> Best,
>>
>> Baptiste Gauthier
>>
>> --
>> Baptiste Gauthier
>> Postdoctoral Research Fellow
>>
>> INSERM-CEA Cognitive Neuroimaging unit
>> CEA/SAC/DSV/DRM/Neurospin center
>> Bât 145, Point Courier 156
>> F-91191 Gif-sur-Yvette Cedex FRANCE
>>
>
>
>
> --
> Baptiste Gauthier
> Postdoctoral Research Fellow
>
> INSERM-CEA Cognitive Neuroimaging unit
> CEA/SAC/DSV/DRM/Neurospin center
> Bât 145, Point Courier 156
> F-91191 Gif-sur-Yvette Cedex FRANCE
>
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