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

[Denis-Alexander Engemann]

I forgot to mention, if you also have EEG data, you cannot use an empty
room noise cov.

2014-10-01 16:31 GMT+02:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:

> 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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