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), 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?
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.
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).
For a ref about NAI, you can look at this chapter:
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 ):
bye
Andrea
Le 14/12/2016 ? 21:43, Alexandre Gramfort a ?crit :
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
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?
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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?