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

[Denis-Alexander Engemann]

Hi Baptiste,

If you have classical ERFs and a 'baseline' I would not rule out computing
the noise cov from baseline segments, In my experience inverse solutions
based on such a 'subject' noise covariance are often more focal. I had
cases where analyses would have failed using an empty room noise cov.
I share your intuition about the classification of the noise covariances
you have sent.
Very roughly you can say that a covariance is better if its matrix plot
looks more diagonal.
As the covariance is used for whitening the data (sensor data + lead field)
you can investigate its quality by computing a whitener and applying it to
the data:

http://martinos.org/mne/stable/auto_examples/plot_evoked_whitening.html

If the majority of signals in the baseline (assumed to represent signals of
non-interest) are not within -1.96 and 1.96 something is wrong. The cov is
actually good if the covariance matrix of the whitened signals looks like
an identity matrix.

Regularization is important when the number of samples used to compute the
noise cov is small.
But it's also important combine different sensort types.

C.f.
http://martinos.org/mne/stable/auto_examples/inverse/plot_make_inverse_operator.html#example-inverse-plot-make-inverse-operator-py


HTH,
Denis

2014-10-01 16:02 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
>
>
>
>  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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