[Mne_analysis] [Freesurfer] question about how to calculate COV for MNE

Joseph Dien jdien07 at mac.com
Mon Apr 7 14:46:37 EDT 2014
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Ah, very good!  Also, I just noticed references to whitening in the python code you linked me to, so now I understand what is being done with this information.  Thanks!

Joe

On Apr 7, 2014, at 3:03 AM, Alexandre Gramfort <alexandre.gramfort at telecom-paristech.fr> wrote:

>> Compute the COV for the two conditions.  When subtracting the two
>> conditions, the accompanying COV matrix (same for both conditions since they
>> were pooled when computing it) is divided by Leff.  Likewise the
>> accompanying degrees of freedom (used if it is subsequently averaged
>> together with another COV matrix per Section 4.17) is set equal to Leff.
> 
> this is correct.
> 
>> Not saying this is a procedure that makes sense to me either, just asking
>> what the intent is of the MNE developers.  Based on your response, this is
>> currently my best guess, although it doesn't make sense to me either since
>> dividing the COV matrix by a single scalar (however computed) shouldn't
>> affect computations about relative noise levels in the channels.  Basically,
>> I'm not sure what is being done with COV so it's hard for me to judge what
>> is a reasonable interpretation of Section 6.3.
> 
> if baseline is pure noise then when you compare conditions by subtraction
> you reduce the variance of the baseline, hence the scaling.
> 
> does it help?
> 
> Alex
> 
>> I appreciate the link to the code, but since I'm a Matlab programmer not a
>> C++ programmer, it's very hard for me to parse the code.  If someone could
>> just tell me what is to be done according to 6.3 when computing a difference
>> wave rather than just what not to do, this would be greatly appreciated.
>> I'm trying to add support for FIFF file format to my Matlab EEG software
>> analysis suite (http://sourceforge.net/projects/erppcatoolkit/) and want to
>> make sure I'm doing it in a manner that would be acceptable to the MNE
>> developers, for the benefit of the users of my package.
>> 
>> Thanks for taking the time to read my question!
>> 
>> Joe
>> 
>> 
>> 
>> On Apr 5, 2014, at 5:51 AM, Alexandre Gramfort
>> <alexandre.gramfort at telecom-paristech.fr> wrote:
>> 
>> hi joe,
>> 
>>  I have a question about how to calculate the COV for the MNE software.
>> As I understand it from Section 4.17 of the 2.7.3 MNE manual, if one is
>> averaging together COV matrices, one weights them by the number of
>> observations going into each one.  I also see from Section 4.17.2 that these
>> are technically not covariance matrices so much as sum-of-squares matrices
>> where the epochs going into each variable has been baseline corrected.
>> 
>>  Assuming my understanding so far is correct, my question is about how to
>> proceed when making linear combinations of COV matrices, as in a difference
>> wave, as discussed in Section 6.3.  Would the following be the correct
>> procedure?
>> 
>>  Compute the COV separately for the two conditions (say standard and rare
>> oddballs).  Subtract the two COV matrices to obtain C0.
>> 
>> 
>> Subtracting two COV matrices is a bad idea. You can make the new cov
>> non positive.
>> 
>> basically the noise cov is just to estimate the noise so any sample you use
>> to get a better estimate the better. There is no condition or contrast
>> at this stage.
>> 
>> for source reconstruction you should use the same noise cov for the two
>> conditions to have the same inverse operator.
>> 
>> you can look at this file to know more about the internals:
>> 
>> https://github.com/mne-tools/mne-python/blob/master/mne/cov.py
>> 
>> HTH
>> Alex
>> 
>> 
>> Then calculate C by
>> dividing by Leff, which in turn is calculated as Leff= (L1*L2)/(L1+L2) where
>> L1 is the number of observations in the first COV matrix and L2 is for the
>> second.
>> 
>>  But if done in this way, the two COV matrices would not be weighted by
>> their relative sample sizes.  So would I also weight them by L1 and L2
>> during the subtraction, as done for averaging per 4.17?  But then what is
>> the purpose of the Leff calculation? Or is the idea that one would calculate
>> a single COV based on both conditions and then one would modify COV by Leff
>> to reflect that the signal-to-noise ratio has been reduced by combining two
>> averages with differing sample sizes?  But in the case where both samples
>> equal, say, 10, Leff would end up equalling 100/20=5.  Dividing C by 5 seems
>> like too much.  Anyway, very confused.  Any guidance would be appreciated.
>> 
>> Respectfully,
>> 
>> Joe
>> 
>> 
>> 
>> 
>> --------------------------------------------------------------------------------
>> 
>> Joseph Dien,
>> Senior Research Scientist
>> Maryland Neuroimaging Center
>> University of Maryland
>> 
>> E-mail: jdien07 at mac.com
>> Phone: 202-297-8117
>> http://joedien.com
>> 
>> 
>> 
>> 
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>> 
>> 
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>> 
>> --------------------------------------------------------------------------------
>> 
>> Joseph Dien,
>> Senior Research Scientist
>> Maryland Neuroimaging Center
>> University of Maryland
>> 
>> E-mail: jdien07 at mac.com
>> Phone: 202-297-8117
>> http://joedien.com
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
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>> 
>> 
>> 
>> 


--------------------------------------------------------------------------------

Joseph Dien,
Senior Research Scientist
Maryland Neuroimaging Center
University of Maryland 

E-mail: jdien07 at mac.com
Phone: 202-297-8117
http://joedien.com














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