<div dir="ltr">Dear Kim,<div><br></div><div>thank you for your response, those are great suggestions. Can I ask if you have any MATLAB code for Woody filtering you could share or any software you know that implements it? I looked it up and it looks very useful - I suspect in a design I described (with within-subject conditions and a between-subject condition) I would do this using the grand-average waveform as a template, i.e., all conditions and all age groups would be corrected to the same template in every iteration? One potential issue with this could be that the N2 looks very different between two within-subject conditions (i.e., it's barely there in a no-conflict condition, and it's much more pronounced in the conflicting condition). Is this something the Woody filter could handle? </div><div><br></div><div>Once again thank you for your help.</div><div><br></div><div>Best,</div><div>Mate </div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, 21 Mar 2019 at 06:33, Whitehead, Kimberley <<a href="mailto:k.whitehead@ucl.ac.uk">k.whitehead@ucl.ac.uk</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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<p class="MsoNormal"><span>Hi<u></u><u></u></span></p>
<p class="MsoNormal"><span><u></u> <u></u></span></p>
<p class="MsoNormal"><span>A Scientific Reports paper from our lab used a similar study design to your own (a within-subject condition and then the between-subjects factor is child/adult), you could have a look for ideas:
DOI: 10.1038/srep28642<u></u><u></u></span></p>
<p class="MsoNormal"><span><u></u> <u></u></span></p>
<p class="MsoNormal"><span>We often use Woody filtering (DOI: 10.1007/BF02474247) to align traces and correct for intra- and inter-subject small differences in latency, but your latency differences might be too big for this.<u></u><u></u></span></p>
<p class="MsoNormal"><span><u></u> <u></u></span></p>
<p class="MsoNormal"><span>But we’ve also recently started using Thomas Koenig’s Ragu software. This software encourages you to check topographic similarity before comparing magnitudes (GFP in the case of Ragu). Because if
the children and adults don’t have the same cortical source configuration of the ‘frontocentral N2’, then that is the more relevant way to compare them, rather than comparing amplitudes which actually derive from different sources.<u></u><u></u></span></p>
<p class="MsoNormal"><span><u></u> <u></u></span></p>
<p class="MsoNormal"><span>Kim<u></u><u></u></span></p>
<p class="MsoNormal"><span><u></u> <u></u></span></p>
<p class="MsoNormal"><span><u></u> <u></u></span></p>
<p class="MsoNormal"><span>Kimberley Whitehead<u></u><u></u></span></p>
<p class="MsoNormal"><span>Research Associate<u></u><u></u></span></p>
<p class="MsoNormal"><span>UCL Dept. of Neuroscience, Physiology and Pharmacology<u></u><u></u></span></p>
<p class="MsoNormal"><span>Tel: 020 7679 3533 (internal 33533)<u></u><u></u></span></p>
<p class="MsoNormal"><span><u></u> <u></u></span></p>
<p class="MsoNormal"><b><span lang="EN-US">From:</span></b><span lang="EN-US"> eeglablist <<a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a>>
<b>On Behalf Of </b>Mate Gyurkovics<br>
<b>Sent:</b> 20 March 2019 12:26<br>
<b>To:</b> <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><br>
<b>Subject:</b> [Eeglablist] ERP analyses across groups with different latencies<u></u><u></u></span></p>
<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">Hi all,<u></u><u></u></p>
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<p class="MsoNormal">I am looking for "best practice" suggestions on how to deal with analyzing group differences in component magnitude when the two groups have different latencies and probably different latency jitter.<u></u><u></u></p>
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<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">I have EEG data from two age groups (kids and adults), and I would like to look at the magnitude of the frontocentral N2 component across the two groups. I have a within-subject condition (conflict vs. no conflict trial) too, so what I'm
mostly interested in is a Trial Type by Age Group interaction. I was originally planning on using mean amplitude but I ran into a problem, namely that the latency of the component is different between the two groups, so using the same time window to capture
the N2 in mean amplitude in both groups seems difficult or impossible because if the window is broad enough to capture the wider peak of the younger group, it's too wide for the adults, and if it suits the adults, it's too narrow for the kids. I am reluctant
to switch to peak amplitude because trial numbers are slightly different across conditions thus noise level differs too.<u></u><u></u></p>
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<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">One option I considered is to create multiple shorter time windows and get the mean from each of those, and then add "Time" as a variable to my analyses, however the choice of time window lengths and number of time windows feels very arbitrary.
I was also considering using an adaptive mean approach as that hopefully would be more robust to slight differences in noise level than a simple peak amplitude measure, but I'm not sure. What do you think? Any suggestions are welcome.<u></u><u></u></p>
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<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">Thank you.<u></u><u></u></p>
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<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">Best,<u></u><u></u></p>
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<p class="MsoNormal">Mate<u></u><u></u></p>
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