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<div class=Section1>
<p class=MsoNormal><span style='font-family:"Verdana","sans-serif";color:#1F497D'>Hi
Allen,<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-family:"Verdana","sans-serif";color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l1 level1 lfo2'><![if !supportLists]><span
lang=EN-US><span style='mso-list:Ignore'>1)<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>With regard to our frequency
domain analysis, I’ve already used FFT to look at general trends in the sample
and delay periods but am interested in plotting the evolution of those
oscillations over trial time. I believe that the cluster analysis and
MonteCarlo simulation are the best method to adopt for quantifying difference
between conditions, but please do correct me if you have other suggestions.<o:p></o:p></span></p>
<p class=MsoListParagraph><span lang=EN-US style='color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:0cm'><span lang=EN-US
style='font-family:"Verdana","sans-serif";color:#1F497D'>The cluster-based
permutation tests are for testing differences between conditions, not for
characterizing temporal evolution (unless you mean by “temporal evolution” a
mean power difference between the first part and the second part of the
trials).<o:p></o:p></span></p>
<p class=MsoListParagraph style='margin-left:0cm'><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:0cm'><span lang=EN-US
style='color:#C0504D'>By temporal evolution I’m referring to the continuous
changes in the TFR map over the course of our long trials. It is
difficult to categorize these into discrete epochs (e.g., early, middle, late delay)
without diluting some of the effect, so I would like to analyze the entire TFR
without any further subdivision. I would like to compare the entire TFR
obtained during different conditions for areas of significant difference.
Am I understanding correctly that the cluster-based permutations are the best
way to approach this question? <o:p></o:p></span></p>
<p class=MsoListParagraph style='margin-left:0cm'><span lang=EN-US
style='font-family:"Verdana","sans-serif";color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:0cm'><span lang=EN-US
style='font-family:"Verdana","sans-serif";color:#1F497D'>Yes.<o:p></o:p></span></p>
<p class=MsoListParagraph style='margin-left:0cm'><span lang=EN-US
style='font-family:"Verdana","sans-serif";color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:0cm'><span lang=EN-US
style='font-family:"Verdana","sans-serif";color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:0cm'><span lang=EN-US
style='font-family:"Verdana","sans-serif";color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l1 level1 lfo2'><![if !supportLists]><span
lang=EN-US><span style='mso-list:Ignore'>2)<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>Another question is about the
edge artifact of using multitapers at ultralow frequencies on relatively short
(57s) data segments. Are multitapers a good approach, and if so could
padding help with the edge artifact? <o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Do you call 57s short? With such a huge trial length, you
definitely need multitapers! I’m not aware of any edge artifact using dpss
tapers.<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#C0504D'>Apologies for the
confusion. This question pertains to a different set of NIRS data for
which I am interested in looking at changes only in the <0.1Hz range.
At frequencies this low, where we get just four or less cycles per entire
trial, the trials are relatively short. In this type of setting, is it
possible to get reliable measures of power with a reasonable temporal
resolution?</span><span lang=EN-US><o:p></o:p></span></p>
<p class=MsoListParagraph><span lang=EN-US style='color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:0cm'><span lang=EN-US
style='font-family:"Verdana","sans-serif";color:#1F497D'>The reliability of
power estimate will depend on the number of trials over which you average your
single-trial power estimates. Your temporal resolution is given by the length
of your analysis window, which is 57s in your case. <o:p></o:p></span></p>
<p class=MsoListParagraph style='margin-left:0cm'><span lang=EN-US
style='font-family:"Verdana","sans-serif";color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l1 level1 lfo2'><![if !supportLists]><span
lang=EN-US><span style='mso-list:Ignore'>3)<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>On a separate note, do you have
any functions available/coming for computing spike-field coherence? Thank
you again.<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>There are no special SFC-functions in FT, as far as I know. In
my own experience, running a coherence analysis on mixed LFP-spike data works
fine. But I have not studied this, and there may be room for improvement …<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif"'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#C0504D'>Under the “Animal
Electrophysiology” section of the tutorial I saw a subheading of “Spike-Field
Coherence” that appears under development. I was just hoping for any new
updates.<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>I’m sorry, no I haven’t finished this project yet.<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Best,<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Eric<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#C0504D'>Thank you so much
again for your help.<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#C0504D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#C0504D'>Thanks,<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#C0504D'>Allen<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Best,<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Eric<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'> <o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Best,<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Allen<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<div>
<p class=MsoNormal><span lang=EN-US style='font-size:10.5pt;font-family:Consolas'>__________________________________________________________________________________</span><span
lang=EN-US><o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Allen Ardestani Email: <a
href="mailto:aardesta@ucla.edu">aardesta@ucla.edu</a><o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Phone: (310) 825-5528<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Medical Scientist Training Program<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>David Geffen School of Medicine at UCLA<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Semel Institute for Neuroscience and Human
Behavior<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>760 Westwood Plaza<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Los Angeles, CA 90095-1759<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>USA<o:p></o:p></span></p>
</div>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<div>
<div style='border:none;border-top:solid #B5C4DF 1.0pt;padding:3.0pt 0cm 0cm 0cm'>
<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 discussion list
[mailto:FIELDTRIP@NIC.SURFNET.NL] <b>On Behalf Of </b>Eric Maris<br>
<b>Sent:</b> Thursday, April 01, 2010 1:51 PM<br>
<b>To:</b> FIELDTRIP@NIC.SURFNET.NL<br>
<b>Subject:</b> Re: [FIELDTRIP] New FieldTrip User Questions<o:p></o:p></span></p>
</div>
</div>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Hi Allen,<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US>I just now came
across the FieldTrip toolbox and have a few questions – I apologize in advance
if I am misusing this discussion forum or if my questions are too
obvious. I am working with a very large dataset of simultaneous LFP,
unit, and NIRS signal from macaques for the purpose of examining time and
frequency dynamics of the signals in various cognitive conditions. I
would like to implement the clustering/bootstrapping methodology outlined in
[Journal of Neuroscience Methods 164 (2007) 177–190] to identify significant
changes in synchronization and phase-locking. <o:p></o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US>The specific
data in question are LFPs recorded while the monkeys perform a working-memory
task, which consists of a 2s visual sample, 20s delay, and subsequent choice
period. A 20s baseline precedes the sample (t=0), which is time-locked to
the animal’s foveation on the visual sample. TF plots are computed using
Morlet wavelets for each trial, averaged across trials, and then normalized
with respect to the average baseline. The first question I’d like to
address is: what time-frequency regions exhibit significant difference between
Correct (left plot) and Incorrect (right plot) trials. <o:p></o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US><img border=0
width=352 height=264 id="Picture_x005f_x0020_2"
src="cid:image001.jpg@01CADDB9.4C4CCBB0"
alt="cid:image002.jpg@01CAA0E0.B7CEC3E0"> <img border=0 width=352
height=264 id="Picture_x005f_x0020_3" src="cid:image002.jpg@01CADDB9.4C4CCBB0"
alt="cid:image003.jpg@01CAA0E0.B7CEC3E0"><o:p></o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US>I have a number
of questions about the appropriateness of applying bootstrapping to our
specific dataset:<o:p></o:p></span></p>
<p class=MsoListParagraph style='margin-left:71.4pt;text-indent:-18.0pt;
mso-list:l0 level1 lfo4'><![if !supportLists]><span lang=EN-US><span
style='mso-list:Ignore'>1)<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>Because we use wavelets for
spectral decomposition (with frequency-dependent changes in spectral and
temporal resolution) is it valid to simply cluster across different
frequencies?<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Yes it is. However, with your 20 sec. trials, I would not go for
the high temporal resolution that he wavelet transform gives you. I would do
one Fourier-based analyses for the first 2 seconds (encoding stage) and another
one for the rest of the trial (retention stage).<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:71.4pt;text-indent:-18.0pt;
mso-list:l0 level1 lfo4'><![if !supportLists]><span lang=EN-US><span
style='mso-list:Ignore'>2)<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>We are interested in comparing
different conditions, where each is computed relative to its own baseline.
Is there anything wrong with using the baselines of the same dataset for the
null distribution as opposed to using a different set of baseline data?<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Do not baseline correct your data. Compare the raw power spectra
for the correct response trials with those of the incorrect response condition.<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:71.4pt;text-indent:-18.0pt;
mso-list:l0 level1 lfo4'><![if !supportLists]><span lang=EN-US><span
style='mso-list:Ignore'>3)<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>The data have been recorded at
1KHz, and this oversampling results in high spatial-frequency noise. Do I
need to do any downsampling/smoothing before applying the statistics?<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>No. However, with such long trials, I would definitely smooth in
the frequency domain using multitapers (also available in Fieldtrip).<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:71.4pt;text-indent:-18.0pt;
mso-list:l0 level1 lfo4'><![if !supportLists]><span lang=EN-US><span
style='mso-list:Ignore'>4)<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>For evaluating the relationship
between channels, we compute the phase-locking value (PLV), which has no
meaningful single-trial measuremetnt. Is there any way to apply
statistical analyses directly to the average TF plots without resorting back to
the individual trials?<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-size:10.5pt;font-family:Consolas;
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Have look at the paper by Maris, Schoffelen, and Fries (2007,
JNM) that describes cluster-based permutation tests for coherence differences.
This may be what you need.<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Best,<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-size:10.5pt;font-family:Consolas'>__________________________________________________________________________________</span><span
lang=EN-US><o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Allen Ardestani Email: <a
href="mailto:aardesta@ucla.edu">aardesta@ucla.edu</a><o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Phone: (310) 825-5528<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Medical Scientist Training Program<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>David Geffen School of Medicine at UCLA<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Semel Institute for Neuroscience and Human
Behavior<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>760 Westwood Plaza<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Los Angeles, CA 90095-1759<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>USA<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p>----------------------------------<o:p></o:p></p>
<p>The aim of this list is to facilitate the discussion between users of the
FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and
EEG analysis.<o:p></o:p></p>
<p>http://listserv.surfnet.nl/archives/fieldtrip.html<o:p></o:p></p>
<p>http://www.ru.nl/fcdonders/fieldtrip/<o:p></o:p></p>
<p><span lang=EN-US>----------------------------------<o:p></o:p></span></p>
<p><span lang=EN-US>The aim of this list is to facilitate the discussion
between users of the FieldTrip toolbox, to share experiences and to discuss new
ideas for MEG and EEG analysis.<o:p></o:p></span></p>
<p><span lang=EN-US>http://listserv.surfnet.nl/archives/fieldtrip.html<o:p></o:p></span></p>
<p><span lang=EN-US>http://www.ru.nl/fcdonders/fieldtrip/<o:p></o:p></span></p>
<p>----------------------------------<o:p></o:p></p>
<p>The aim of this list is to facilitate the discussion between users of the
FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and
EEG analysis.<o:p></o:p></p>
<p>http://listserv.surfnet.nl/archives/fieldtrip.html<o:p></o:p></p>
<p>http://www.ru.nl/fcdonders/fieldtrip/<o:p></o:p></p>
<p><span lang=EN-US>----------------------------------<o:p></o:p></span></p>
<p><span lang=EN-US>The aim of this list is to facilitate the discussion
between users of the FieldTrip toolbox, to share experiences and to discuss new
ideas for MEG and EEG analysis.<o:p></o:p></span></p>
<p><span lang=EN-US>http://listserv.surfnet.nl/archives/fieldtrip.html<o:p></o:p></span></p>
<p><span lang=EN-US>http://www.ru.nl/fcdonders/fieldtrip/<o:p></o:p></span></p>
<p>----------------------------------<o:p></o:p></p>
<p>The aim of this list is to facilitate the discussion between users of the
FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and
EEG analysis.<o:p></o:p></p>
<p>http://listserv.surfnet.nl/archives/fieldtrip.html<o:p></o:p></p>
<p>http://www.ru.nl/fcdonders/fieldtrip/<o:p></o:p></p>
</div>
</body>
</html>
<p>----------------------------------</p>
<p>The aim of this list is to facilitate the discussion between users of the FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis.</p>
<p> http://listserv.surfnet.nl/archives/fieldtrip.html</p>
<p> http://www.ru.nl/fcdonders/fieldtrip/</p>