Dear Dwight,<div><br></div><div>I don't know what CDA stands for but I do have a paper about EEG study using visual hemifield presentation so hopefully I understand what you mean. It seems you are saying you don't see ipsi/contralateral presentation effect. My understanding is that this effect is super robust and you should not miss it. If, for example, you mixed up the event markers and randomly choosing ipsi/contralateral event you can miss it. Another possibility would be that stimulus presentation was too close to the central area AND too dim AND too blurry; otherwise how can you destroy such robust effect? Why don't you check these things first.</div>
<div><br></div><div>Makoto<br><br><div class="gmail_quote">2013/3/7 Dwight Peterson <span dir="ltr"><<a href="mailto:dwight.peterson23@gmail.com" target="_blank">dwight.peterson23@gmail.com</a>></span><br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Hi all,<div><br></div><div><br></div><div> For those of you familiar with the CDA, please help if possible. I'm working on a project exploring the CDA for visual working memory and my data thus far show shockingly similar waveforms for contralateral and ipsilateral activity (my understanding is contralateral should be much higher in amplitude and negative going). I have 4 different conditions in my experiment and I'm am including in my analysis only activity associated with CORRECT trials. I have 7 channels with 6 electrode sites (Channel 1 corresponds to a photo-diode that I use to mark the onset of my stimulus/trial). I have these few sites b/c I'm working with an 8 channel "home-brew" sort of EEG system. Ch 2 = P7, Ch 3 = P8, Ch 4 = PO7, Ch 5 = PO8, Ch 6 = P1, Ch 7 = P2. </div>
<div><br></div><div> I've created an event list that creates 8 bins that correspond to Left Hemifield trials and Right Hemifield trials for each of my 4 conditions that correspond to my event type (unique identifier values) as shown below:</div>
<div><br></div><div><p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier"> 70 "ss_two_r" 1 "two_objects_RVHF" </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier"> 71 "ss_two_l" 2 "two_objects_LVHF" </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier"> 80 "ss_three_r" 3 "three_objects_RVHF" </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier"> 81 "ss_three_l" 4 "three_objects_LVHF" </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier"> 90 "ss_three_apart_r" 5 "three_objects_apart_RVHF"</p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier"> 91 "ss_three_apart_l" 6 "three_objects_apart_LVHF"</p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier"> 94 "ss_three_prox_r" 7 "three_objects_prox_RVHF" </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier"> 95 "ss_three_prox_l" 8 "three_objects_prox_LVHF"</p></div><div><br></div><div>Then after epoching, running artifact detection, and computing averaged ERP waveforms for each of these bins, I created a new ERPset from Bin Operations using the Independent Transformations "mode" with the following formulas to extract the contralateral and ipsilateral waveforms: </div>
<div><br></div><div><p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">LH = [2 4 6] </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">RH = [3 5 7] </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">nb1 = 0.5*b1@LH + 0.5*b2@RH label average contra two objects </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">nb2 = 0.5*b1@RH + 0.5*b2@LH label average ipsi two objects </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">nb3 = 0.5*b3@LH + 0.5*b4@RH label average contra three objects</p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">nb4 = 0.5*b3@RH + 0.5*b4@LH label average ipsi three objects </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">nb5 = 0.5*b5@LH + 0.5*b6@RH label average contra three apart </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">nb6 = 0.5*b5@RH + 0.5*b6@LH label average ipsi three apart </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">nb7 = 0.5*b7@LH + 0.5*b8@RH label average contra three prox </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">nb8 = 0.5*b7@RH + 0.5*b8@LH label average ipsi three prox </p></div><div><br></div><div><br></div><div>Then I get the CDA difference waveforms for each condition by recursively updating the new Contra Ipsi ERPset by using the following formula</div>
<div><br></div><div><p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">b9 = b1-b2 label CDA Two Objects </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">b10 = b3-b4 label CDA Three Objects</p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">b11 = b5-b6 label CDA Three Apart </p>
<p style="margin:0.0px 0.0px 0.0px 0.0px;font:10.0px Courier">b12 = b7-b8 label CDA Three Prox </p><div><br></div><div> Perhaps I am just seeing an extremely NULL result and lack of a difference between contra and ipsi waveforms for each condition, but I've tried a variety of things and double checked these formulas (which seem to be correct given my event types and bins, etc.). Impedances are below 5 K<span style="font-family:sans-serif;font-size:13px;line-height:19px">Ω and aside from the lack of a difference between contra and ipsi, the data looks pretty clean. </span></div>
<div><br></div><div>For anybody who has done this type of research before, do you have any thoughts? </div><div><br></div><div>Thanks so much!!! </div><span class="HOEnZb"><font color="#888888"><div><br></div><div><br>
</div><div>--Dwight--</div><div><br></div>-- <br>
Dwight J. Peterson, M.A.<br>Graduate Assistant<br>Cognitive and Brain Sciences<br>Department of Psychology/296<div>Memory and Brain Laboratory<br>University of Nevada, Reno<br>Reno, NV 89557<br>Cellular: <a href="tel:319-231-8596" value="+13192318596" target="_blank">319-231-8596</a></div>
<div>Laboratory: <a href="tel:775-682-8667" value="+17756828667" target="_blank">775-682-8667</a></div>
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-- <br>Makoto Miyakoshi<br>JSPS Postdoctral Fellow for Research Abroad<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br>
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