[Mne_analysis] questions regarding noise-cov matrix (NVM)

Jeff Eriksen eriksenj at ohsu.edu
Wed Jun 13 19:52:27 EDT 2012
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Dan,

All good suggestions, I will try them out. Regarding Matlab access to the
NCM, I see that I had pulled up mne_read_cov, not mne_read_noise_cov. The
former has things like "node" and "kind" that were beyond me. Thanks,

-Jeff

On 6/13/12 4:40 PM, "Dan G Wakeman" <dgwakeman at gmail.com> wrote:

>
>On 2012 Jun 13, at 19:08 , Jeff Eriksen wrote:
>
>> One more question: My data is 250 seconds long, and I am using the
>>entire recording to calculate the NCM. Should I use only a portion?
>
>It depends. How clean is the data. You absolutely should only use the
>cleanest piece of data you should check (visually) that it looks good the
>whole way with identical filter and projection settings as used when
>calculating. Any noise or artifacts should be avoided.
>
>> -Jeff
>> 
>> From: Jeff Eriksen <eriksenj at ohsu.edu<mailto:eriksenj at ohsu.edu>>
>> To: 
>>"mne_analysis at nmr.mgh.harvard.edu<mailto:mne_analysis at nmr.mgh.harvard.edu
>>>" 
>>><mne_analysis at nmr.mgh.harvard.edu<mailto:mne_analysis at nmr.mgh.harvard.ed
>>>u>>
>> Subject: [Mne_analysis] questions regarding noise-cov matrix (NVM)
>> 
>> I have been having some difficulties with the inverse results from my
>>256-channelEEG, some of which may stem from my noise-covariance matrix,
>>and have come up with the following questions:
>> 
>> For MEG, the manual suggests recording some MEG without a subject in
>>the loop ­ room noise, basically. It also suggests for epilepsy work to
>>use a segment of MEG (or EEG) that has no epileptiform activity. I am
>>doing continuous EEG, with no discrete events to speak of, so I wonder
>>if I should do a subject-less recording and use that as the basis of my
>>noise-cov matrix. I would do that by applying the electrode Net to a
>>styrofoam head covered with a saline soaked towel.
>
>I don't think that would work (it may even create more problems). There
>are a couple of options you have
>
>1 stick with a diagonal noise covariance
>2 try using a different set of filtering on your EEG data to remove the
>brain activity but leave the other parts of noise e.g. 100Hz - 200Hz when
>calculating the noise covariance matrix
>	If you were to want to try this, you should definitely do some testing
>to see you are getting reasonable results (having some evoked data would
>help greatly for this testing). You should also definitely check the time
>frequency spectrum.
>
>> 
>> What is the use of the noise-cov supposed to accomplish in practical
>>terms?
>
>I'll leave this to experts better at explaining this than me. (someone
>please fill in!)
>
>> 
>> It has been suggested that I try the ‹diagnoise switch if my inverse
>>results are not matching my scalp topography (they are not, to my eye).
>>I did that, and it seems to have helped a bit, but why? Why does the
>>manual say that most users should never need to use this option?
>
>Because in very general terms a full noise covariance matrix produces a
>much more elegant and better regularized inverse solution.
>> 
>> I would like to read the NCM matrix into Matlab to examine it, but I
>>cannot understand the MNE Matlab routines (mne- and fif-) that allow me
>>to do that ­ too many undefined terms. Can someone give me an explicit
>>set of instructions of how to look at the NCM in Matlab?
>
>Well, could you explain what terms aren't defined? You only need one
>function to read the file the help output is below:
>
>help mne_read_noise_cov
>  [cov] = mne_read_noise_cov(fname)
> 
>  Reads a noise-covariance matrix from a fiff file
> 
>  fname      - The name of the file
>
>The matlab structure is described in table 10.29 in the MNE manual.
>
>> 
>> Can I avoid the use of the NCM altogether, or set it to a diagonal
>>identity matrix?
>
>You can use the diagonal, though your solution won't be as well
>regularized as it could be.
>> 
>> Thanks
>> --Jeff
>> 
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