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

[dgw]

That and other "Unless" statements, is why I strongly recommend
treating all your data identically. One cannot know how good each
person's system and default SSPs etc. are, but if you treat all data
the same (which has no downside) you at least can expect the same
responses.

In the plots Baptiste originally sent the "bad?" plot looked as though
there were artifacts in the empty room recording, which created the
the strikingly different plot. Examining the raw data is a must.

HTH
D

On Wed, Oct 1, 2014 at 12:00 PM, Ghuman, Avniel <ghumana at upmc.edu> wrote:
> Hi Hari,
>
> That is in part because the noise floor of SQUIDs are frequency dependent.
> However, the question is not whether the covariance matrix is biased
> towards being influenced by low frequencies, but whether or not the
> overall shape of the covariance matrix differs across frequencies. Unless
> you have multiple sources of environmental noise with different spatial
> distributions in sensor space, each of which has a different frequency
> dependance, there should be no effect of band pass filtering on the
> covariance matrix built from empty room. Hopefully though, the intrinsic
> SSP projections that are attached to the raw fif files would address much
> of that.
>
> Best wishes,
> Avniel
>
> On 10/1/14 11:13 AM, "Hari Bharadwaj" <hari at nmr.mgh.harvard.edu> wrote:
>
>>Whether the band pass makes a difference or not with empty room data in
>>my experience depends on the band that is included... The spatial
>>covariance appears to be dominated by he lower frequencies...
>>
>>Regardless, I can't think of any reason to not process the noise data
>>segments in the exact same way as the data segments of interest.
>>
>>Hari
>>
>>> On Oct 1, 2014, at 11:07 AM, "Ghuman, Avniel" <ghumana at upmc.edu> wrote:
>>>
>>> To answer this question:
>>>
>>> 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?
>>>
>>> My experience is that, if you are using empty room data, the band pass
>>>makes essentially no difference. With baseline segments it can make a
>>>little difference, but even here the difference is minimal between
>>>broadband and band passed. Definitely though, at least broad-band pass
>>>the empty room and/or baseline though to remove high frequency noise and
>>>very low frequency drift (1-50 Hz or 1-100 Hz or something of that
>>>nature) and apply any SSP projectors you use in your real data.
>>>
>>> Best wishes,
>>> Avniel
>>>
>>> From: Denis-Alexander Engemann
>>><denis.engemann at gmail.com<mailto:denis.engemann at gmail.com>>
>>> Reply-To: Discussion and support forum for the users of MNE Software
>>><mne_analysis at nmr.mgh.harvard.edu<mailto:mne_analysis at nmr.mgh.harvard.edu
>>>>>
>>> Date: Wednesday, October 1, 2014 10:31 AM
>>> To: Discussion and support forum for the users of MNE Software
>>><mne_analysis at nmr.mgh.harvard.edu<mailto:mne_analysis at nmr.mgh.harvard.edu
>>>>>
>>> Subject: Re: [Mne_analysis] What is a "good" noise covariance matrix?
>>>
>>> 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_op
>>>erator.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<mailto: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
>>>
>>>
>>>
>>> [https://ssl.gstatic.com/docs/doclist/images/icon_11_image_list.png]
>>>bad?.png<https://docs.google.com/file/d/0B_eZxstAMJQscGpiOF9VY00yLWc/edit
>>>?usp=drive_web>
>>>
>>> [https://ssl.gstatic.com/docs/doclist/images/icon_11_image_list.png]
>>>good?.png<https://docs.google.com/file/d/0B_eZxstAMJQsY01WdGlJbENHa0U/edi
>>>t?usp=drive_web>
>>>
>>>
>>> 2014-10-01 14:05 GMT+02:00 Baptiste Gauthier
>>><gauthierb.ens at gmail.com<mailto: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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