[Mne_analysis] question about LCMV

[daisy]

Thank you for your help！

According to the definition of neural activity index (NAI)，it is the power normalized with an estimate of noise. So the output of LCMV is likely to be NAI，but if I want to know the way of normalization in lcmv, I have to look the details of the code. Right?

-Best,
Xiaoxu
> On Dec 15, 2016, at 3:08 AM, Andrea Brovelli <andrea.brovelli at univ-amu.fr> wrote:
> 
> Hi all,
> 
> in fact, I did not look into the details of the lcvm code. For what concerns dics, I think the output is the ratio of powers (data/noise), that is what is normally called "neural activity index" NAI. In don't think the 1997 Van Veen paper has defined the NAI (https://www.lsv.uni-saarland.de/fileadmin/teaching/dsp/ss15/BF_VeenBuckley.pdf <https://www.lsv.uni-saarland.de/fileadmin/teaching/dsp/ss15/BF_VeenBuckley.pdf>). For a ref about NAI, you can look at this chapter:
> 
> http://www.ccs.fau.edu/~fuchs/pub/Beamer.pdf <http://www.ccs.fau.edu/~fuchs/pub/Beamer.pdf>
> Or the webpage of Fieldtrip:
> 
> http://www.fieldtriptoolbox.org/tutorial/beamformer#neural_activity_index <http://www.fieldtriptoolbox.org/tutorial/beamformer#neural_activity_index>
> More generally, it would be good to have the possibility in MNE to have different normalization techniques (in addition to "None", in some case people may prefer not to normalise wrt noise). Keeping in mind that power values are not normally distributed (they are chi-squared), some ways to normalise are:
> 
> 1) dB = 10 * log10(data/noise)
> 
> 2) z-score = [data - mean(noise)] / std(noise)
> 
> 3) z-score of logs = [log(data) - mean(log(noise))] / std(log(noise))  (the log-transform make power values approx. gaussian, as sqrt-transform)
> 4) ERD/S = [data - mean(noise)] / mean(noise)
> 
> ... just to cite a few
> Some inspiration here (lines 198-207 ):
> 
> https://github.com/fieldtrip/fieldtrip/blob/master/ft_freqbaseline.m <https://github.com/fieldtrip/fieldtrip/blob/master/ft_freqbaseline.m>
> bye
> 
> Andrea
> 
> 
> 
> Le 14/12/2016 à 21:43, Alexandre Gramfort a écrit :
>> Hi,
>> 
>> cc Andrea would recently looked a lot at this code.
>> 
>> stc.data contains the output of the filter and taking the magnitude if non
>> fixed orientation is used. I am not sure how to call this. Maybe Andrea can tell
>> you how he refers to this.
>> 
>> if you apply LCMV to 2 conditions you should use the same noise cov and data cov
>> in which case the filters are the same and the stc.data are definitely comparable.
>> 
>> my 2c
>> Alex
>> 
>> On Wed, Dec 14, 2016 at 6:21 PM, daisy <smartcandies at 163.com <mailto:smartcandies at 163.com>> wrote:
>> Hi, experts
>> 
>> I have some questions  when I computed LCMV beamformer on evoked data. Could anyone help me?
>> 
>> (1)I use this command：stc=lcmv(evoked_con1,forward,noise_cov,data_cov,reg=0.01). Then I can plot the source time courses(stc). The xlabel is time(ms). But what is the ylabel? Because the beamformer weight was normalized by noise, according to the paper of Van Veen in 1997, the ylabel should be neural activity index. But for one example in the gallery(http://martinos.org/mne/stable/auto_examples/inverse/plot_lcmv_beamformer_volume.html?highlight=lcmv <http://martinos.org/mne/stable/auto_examples/inverse/plot_lcmv_beamformer_volume.html?highlight=lcmv>), the ylabel is LCMV value. Is the ‘lcmv value’ same with ’neural acitvity index’ or ‘pseudo-z value’(Robinson and Vrba 1999)?
>> (2) If I apply LCMV beamformer on evoked data of two conditions, then I can get two stc files. Can I compare them directly? For example, calculate the difference of time course of these two conditions in given time and location? Is it meaningful?
>> 
>> Thank you!
>> 
>> -Best
>>  Xiaoxu
>> 
>> 
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