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

Wed Oct 1 11:07:30 EDT 2014

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_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<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/edit?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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