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<p class="MsoNormal"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D">Dear Jorn, Johanna,<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D">Thank you for your replies. I was making a couple of mistakes. I wasn’t using the parameters correctly and now use cfg.lcmv.<parameter> which works. The mismatch
between the number of channels and the size of the filter was due to the strange situation that ASA channel labels sometimes uppercase letters (from the electrode file) and sometimes lowercase (from the header file). Fieldtrip does not understand AFZ but knows
AFz for example. <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D">The routine ft_prepare_vol_sense.m thus discarded half of the channels.
<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D">I found the voxel with the maximal power and reconstructing the dipole moment at that particular source for each trial using the following code which I found in
an earlier post somewhere:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New","serif"">trialN = size(timelock0.trial,1);</span><span lang="EN-US" style="font-family:"Courier New","serif""><o:p></o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New","serif";color:blue">for</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New","serif""> trial_tel = 1:trialN</span><span lang="EN-US" style="font-family:"Courier New","serif""><o:p></o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New","serif""> sourceDiff.trial(trial_tel).mom = sourceDiff.avg.filter{maxind}*data0FIC.trial{trial_tel};</span><span lang="EN-US" style="font-family:"Courier New","serif""><o:p></o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New","serif";color:blue">end</span><span lang="EN-US" style="font-family:"Courier New","serif""><o:p></o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D">where maxind is the index of the voxel with maximum power. Timelock0 takes each trial with a window of [-0.5 0.5] s around each event.
I will use a SVD to determine the power of the source for each trial separately.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D">Thanks again for your help!<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Calibri","sans-serif";color:#1F497D">Alistair<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><a name="_MailEndCompose"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></a></p>
<p class="MsoNormal"><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Tahoma","sans-serif"">From:</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Tahoma","sans-serif""> fieldtrip-bounces@science.ru.nl [mailto:fieldtrip-bounces@science.ru.nl]
<b>On Behalf Of </b>Johanna Zumer<br>
<b>Sent:</b> Tuesday, October 30, 2012 10:37 AM<br>
<b>To:</b> FieldTrip discussion list<br>
<b>Subject:</b> Re: [FieldTrip] LCMV beamformer source reconstruction<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Dear Alistair,<o:p></o:p></p>
<div>
<p class="MsoNormal"><o:p> </o:p></p>
</div>
<div>
<p class="MsoNormal">In addition to the informative response from Jörn, I just wanted to add:<o:p></o:p></p>
<div>
<p class="MsoNormal"><o:p> </o:p></p>
</div>
<div>
<p class="MsoNormal">One tiny thing I note is that you type cfg.lmvc.lambda which should be cfg.lcmv.lambda.<o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><o:p> </o:p></p>
</div>
<div>
<p class="MsoNormal" style="margin-bottom:12.0pt">Why is your filter of size 3x65? Shouldn't it be 3x125 (3 source orientations x 125 EEG channels)? <o:p></o:p></p>
</div>
<div>
<p class="MsoNormal">Cheers,<o:p></o:p></p>
</div>
<div>
<p class="MsoNormal">Johanna<o:p></o:p></p>
</div>
<p class="MsoNormal"><o:p> </o:p></p>
<div>
<p class="MsoNormal">2012/10/30 "Jörn M. Horschig" <<a href="mailto:jm.horschig@donders.ru.nl" target="_blank">jm.horschig@donders.ru.nl</a>><o:p></o:p></p>
<div>
<div>
<p class="MsoNormal">Dear Alistair,<br>
<br>
I am not quite sure whether I understand your request correctly, but you are looking for help to get the maximum activity per trial in your source reconstructed data? I guess your confusion arises because of the different subfields, so here a short explanation:<br>
<br>
Every dipole has strengths in 3 directions (think of it as magnetic field strength in the x,y and z direction), this is stored in .mom<br>
In order to get it down to one number instead of three, there are different possibilities, but what's roughly happening by default is that an SVD is computed then the principal direction is taken, i.e. the strongest of these three vectors. This is what you
find back in .pow. The filter matrix is the filter per grid position to get from your sensor data to the source data. Noise is an approximation of the noise level per trial.<br>
<br>
So, if you want to get the maximum activity, I would suggest to take [~, idx] = max(sourceDiff.avg.pow{i}), where idx then is the the grid position of maximal power. If you are interested in the maximum over e.g. the posterior part of the brain, you should
limit your search. You can best do that by using sourceDiff.pos and an atlas.<br>
<br>
If that does not answer your question, feel free to clarify your request ;)<br>
<br>
Best,<br>
Jörn<o:p></o:p></p>
<div>
<div>
<p class="MsoNormal"><br>
<br>
On 10/25/2012 10:40 PM, Alistair Vardy - 3ME wrote:<o:p></o:p></p>
</div>
</div>
</div>
<blockquote style="margin-top:5.0pt;margin-bottom:5.0pt">
<div>
<div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US">Hi all,</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US">I trying to reconstruct the activity in a source using the LCMV beamformer with EEG data. My code follows several examples that stop at the source localization.
Unfortunately, I am unable to reconstruct the source. </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US">The data is a series of 198 button presses, self-paced. The two time locked data sets are pre- and post-event windows. The data is filtered in the beta band (13-30
Hz). There are 128 EEG channels, three of which are excluded due to excessive noise. The data was re-referenced to the common average. The source that provides the normalized difference in power between the two time windows has a field avg with subfields,
filter, noise, pow, and mom:</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US">sourceDiff =
</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> dim: [17 13 14]</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> time: [1x819 double]</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> pos: [3094x3 double]</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> inside: [1x1567 double]</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> outside: [1x1527 double]</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> method: 'average'</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> avg: [1x1 struct]</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> cfg: [1x1 struct]</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US">sourceDiff.avg</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US">ans =
</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> pow: [1x3094 double]</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> mom: {1x3094 cell}</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> noise: [1x3094 double]</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> filter: {1x3094 cell}</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US">The moment entries are sized 3 x 819, the filter 3 x 65.</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US">I hope someone can help me with the code required to reconstruct the source at the voxel with the largest power during the entire duration of each trial. The
code used to determine the source is below. MRI, head model and leadfield were computed as well but are not included in the code below.</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US">Kind regards,</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US">Alistair</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
channel = {'EEG', '-AFF1', '-AFZ', '-AF1'};<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span style="font-family:"Courier New","serif""> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg1 = [];</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg1.keeptrials = 'yes';</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg1.covariance = 'yes';</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg1.channel = channel;</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">dataPre = ft_redefinetrial(cfg1, data1FIC);</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">timelock1 = ft_timelockanalysis(cfg1, dataPre);</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg2 = []; </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg2.keeptrials = 'yes';</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg2.covariance = 'yes';</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg2.channel = channel;</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">dataPost = ft_redefinetrial(cfg2, data2FIC);</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">timelock2 = ft_timelockanalysis(cfg2, dataPost);</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US" style="font-family:"Courier New","serif""> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">%% Source analysis</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg = [];</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg.grid = grid;</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg.hdmfile = volname;</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg.elec = sens;</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg.vol = vol;</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg.method = 'lcmv';</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg.channel = channel;</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg.lcmv.keeptrials = 'yes';</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">% cfg.lcmv.projectnoise = 'yes';</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg.lmvc.lambda = '5%';</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">cfg.lcmv.keepfilter = 'yes';</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">[source1] = ft_sourceanalysis(cfg, timelock1);</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">[source2] = ft_sourceanalysis(cfg, timelock2);</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">sourceDiff = source2;</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US">sourceDiff.avg.pow = (source2.avg.pow - source1.avg.pow) ./ source1.avg.pow;</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-autospace:none">
<span lang="EN-US" style="font-family:"Courier New","serif""> </span><o:p></o:p></p>
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<pre>_______________________________________________<o:p></o:p></pre>
<pre>fieldtrip mailing list<o:p></o:p></pre>
<pre><a href="mailto:fieldtrip@donders.ru.nl" target="_blank">fieldtrip@donders.ru.nl</a><o:p></o:p></pre>
<pre><a href="http://mailman.science.ru.nl/mailman/listinfo/fieldtrip" target="_blank">http://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a><o:p></o:p></pre>
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<pre>-- <o:p></o:p></pre>
<pre>Jörn M. Horschig<o:p></o:p></pre>
<pre>PhD Student<o:p></o:p></pre>
<pre>Donders Institute for Brain, Cognition and Behaviour <o:p></o:p></pre>
<pre>Centre for Cognitive Neuroimaging<o:p></o:p></pre>
<pre>Radboud University Nijmegen <o:p></o:p></pre>
<pre>Neuronal Oscillations Group<o:p></o:p></pre>
<pre>FieldTrip Development Team<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre>P.O. Box 9101<o:p></o:p></pre>
<pre>NL-6500 HB Nijmegen<o:p></o:p></pre>
<pre>The Netherlands<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre>Contact:<o:p></o:p></pre>
<pre>E-Mail: <a href="mailto:jm.horschig@donders.ru.nl" target="_blank">jm.horschig@donders.ru.nl</a><o:p></o:p></pre>
<pre>Tel: <a href="tel:%2B31-%280%2924-36-68493" target="_blank">+31-(0)24-36-68493</a><o:p></o:p></pre>
<pre>Web: <a href="http://www.ru.nl/donders" target="_blank">http://www.ru.nl/donders</a><o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre>Visiting address:<o:p></o:p></pre>
<pre>Trigon, room 2.30<o:p></o:p></pre>
<pre>Kapittelweg 29<o:p></o:p></pre>
<pre>NL-6525 EN Nijmegen<o:p></o:p></pre>
<pre>The Netherlands<o:p></o:p></pre>
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_______________________________________________<br>
fieldtrip mailing list<br>
<a href="mailto:fieldtrip@donders.ru.nl">fieldtrip@donders.ru.nl</a><br>
<a href="http://mailman.science.ru.nl/mailman/listinfo/fieldtrip" target="_blank">http://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a><o:p></o:p></p>
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