<div dir="ltr">Dear Diego,<div>Thank you very much for your reply!</div><div><br></div><div>I am familiar with these two studies (which I came to know through the wonderful Electrical Neuroimaging book by Cristoph Michel.</div>
<div>Unfortunately, the data I have is clinical data that was recorded using only 19 electrodes.</div><div><br></div><div>Localization precision is indeed too low in that case, but I am hoping it would suffice for analyzing certain aspects of the signal (e.g. power spectrum) on a large enough ROI, or a network of ROIs that covers a large portion of the brain.</div>
<div><br></div><div>Thank you once again,</div><div>roey</div></div><div class="gmail_extra"><br><br><div class="gmail_quote">On Wed, Jul 23, 2014 at 5:35 PM, Lozano Soldevilla, D. (Diego) <span dir="ltr"><<a href="mailto:d.lozanosoldevilla@fcdonders.ru.nl" target="_blank">d.lozanosoldevilla@fcdonders.ru.nl</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div style="font-family:Times New Roman;font-size:12pt;color:#000000"><div><div>Dear Roey,</div><div><br></div><div>
In my opinion it's definitely not a good idea to compute MNE using 19 sensors. There are studies that have found a drastic localization precision from 31 to 63 electrodes and further improvements till 123:</div><div><br>
</div><a href="http://www.ncbi.nlm.nih.gov/pubmed/15351361" target="_blank">http://www.ncbi.nlm.nih.gov/pubmed/15351361</a> (see figure 1)<br><a href="http://www.ncbi.nlm.nih.gov/pubmed/12495765" target="_blank">http://www.ncbi.nlm.nih.gov/pubmed/12495765</a><div>
<br></div><div>Although it's very difficult to know the "minimum" number of electrodes needed to accurately localize a given source (it depends on the strength of the source you want to localize, source reconstruction algorithm, data noise...), 19 electrodes are too low to trust the results you can get. </div>
<div><br></div><div>best,</div><div><br></div><div>Diego</div><div><br></div></div><div><br><hr style="color:rgb(0,0,0);font-family:'Times New Roman';font-size:12pt"><pre style="color:rgb(0,0,0);font-family:'Times New Roman';word-wrap:break-word;white-space:pre-wrap">
>From roeysc at <a href="http://gmail.com" target="_blank">gmail.com</a> Mon Jul 21 11:21:32 2014
From: roeysc at <a href="http://gmail.com" target="_blank">gmail.com</a> (Roey Schurr)
Date: Mon, 21 Jul 2014 12:21:32 +0300
Subject: [FieldTrip] MNE Source Reconstruction Sanity Check
Message-ID: <<a href="mailto:CAHm4wZBRYo4fV63EL9yXaAQ_W43cHF_8J2b%2BrNyzd55x4aRviw@mail.gmail.com" target="_blank">CAHm4wZBRYo4fV63EL9yXaAQ_W43cHF_8J2b+rNyzd55x4aRviw@mail.gmail.com</a>>
Dear fieldtrippers,
I want to do a sanity check on mne source reconstruction.
I'm working on continuous EEG recordings (19 electrodes), estimating the
source reconstruction activity using the *mne* (minimum norm estimate)
method, a *template MRI* (Colin27) and a *singlesphere* headmodel. As a
sanity check for the source reconstruction itself, I wanted to compare
conditions in which I could estimate the loci of significant changes, e.g.:
rest vs movement of the hand, moving the right hand vs the left hand, etc.
I have about 60 seconds of recording for each condition.
What I did was:
1) Segment the recording of each condition into many "trials" of 2 seconds
each.
2) For each trial, average the activity in each of the 90 ROIs of the aal
atlas (I excluded the cerebellum from the source reconstruction).
I was wondering what comparison would be best in this case. Since this is
not Evoked Responses data, I find it hard to find relevant ideas, and would
like to hear your thoughts.
1) I did a frequency analysis (mtmfft) in conventional bands of interest
and ran ft_freqstatistics on the resulting structures (using ttest2 and the
bonferoni correction for the multiple comparison problem). This gave some
results, however for most conditions they are not very encouraging (the
ROIs that showed significant differences were not close to those that I
have assumed).
*QUESTION 1*: do you think this is a proper method? Note that I did not use
a frequency based source reconstruction in the first place, because I'm
ultimately interested in the time course in the source space.
2) I was wondering if a cluster based permutation test is impossible to use
here, since this is a continuous recording, so clustering according to time
adjacency seems irrelevant.
*QUESTION 2*: is it possible to use a cluster based statistical test here?
If so, it could be better than a-priori averaging the source activity in
the atlas ROIs, which could mask some of the effects, if they are located
in a small area.
3) Another possibility is looking at the data itself. Unfortunately I
encountered some problems using ft_sourcemovie, though this is a subject
for a different thread.
Any thoughts and advice are highly appreciated!
Thank you for taking the time,
roey</pre></div></div></div><br>_______________________________________________<br>
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