<div dir="ltr">Dear Ana,<div><br></div><div style>Ok I got your datasets and diagnosed what was wrong.</div><div style>I found several things not-so-correct (sorry for telling you this!) but the worst thing was that your ICA decomposition is ill. Take a look at component activations from Plot-Component activations (scroll). Notice the scale-it says 8.82E+05, which is 3 or 4 digits too large for IC activations.</div>
<div style><br></div><div style>This is typically caused by rank issue: in my experience, when I copied weight matrix to the rank-reduced data this happened. Rerun ICA and confirm that your IC activations have proper amplitude ranges.</div>
<div style><br></div><div style>Makoto</div></div><div class="gmail_extra"><br><br><div class="gmail_quote">2013/4/18 Ana Navarro Cebrian <span dir="ltr"><<a href="mailto:anavarrocebrian@gmail.com" target="_blank">anavarrocebrian@gmail.com</a>></span><br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi Makoto,<br><br><div class="gmail_quote"><div class="im"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div dir="ltr"><div><div>Maybe you want to try my toolbox to backproject cluster ERPs to channels. I'll send it to you in the separate mail.</div>
</div></div></blockquote></div><div>I'd love to. Thank you. </div><div class="im"><div><br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div>
<div>I saw it. It is quite strange indeed. I want to diagnose the situation.</div>
<div>At least I can tell that your 'outermost envelope' (shown in thick black) is surpassed by other envelopes, which is not normal. That is like saying some of your clusters have larger values than the sum of all (it's not exactly like this but pretty much about it). Can you think of any reason why you see this?</div>
<div><br></div><div>If you can't find anything wrong by yourself, I need to ask you to either 1. transfer all of your data to our server or 2. video conference by sharing your desktop. You can choose whichever you want. Sorry for inconvenience.</div>
</div></div></blockquote></div><div>That is strange. I have no idea what could be wrong. I tried clustering again with a greater (20) and a smaller (2) number of clusters. I get the same two clusters both ways and the outermost envelope is always surpassed by the envelopes of these two clusters, even when those two clusters are the only ones in the analysis. The only thing I can think of is that these two clusters are of an opposite signal and they kind of cancel each other out, so they are greater alone than together. This would be kind of related to the problem of having all the electrodes included. I'm not sure if that makes sense.</div>
<div>I'm happy to transfer my data so you can take a look at it. I just looked at only 3 subjects and I get the same effect. I could transfer those instead of the entire group if you want.</div><div>Let me know.</div>
<div>Thanks,</div><div>Ana</div><div><div class="h5"><div> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><span><font color="#888888">
<div><br></div><div>Makoto </div></font></span></div><div><div><div class="gmail_extra"><br><br><div class="gmail_quote">2013/4/10 Ana Navarro Cebrian <span dir="ltr"><<a href="mailto:anavarrocebrian@gmail.com" target="_blank">anavarrocebrian@gmail.com</a>></span><br>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div><div><div><div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif">
>Currently this is not supported. I see how to do it though.</div><div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif">
I would suggest that you exclude clusters of non-interest, or specify the clusters to use, so that your outermost envelope is consist of only necessary ones.</div><div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif">
<br></div></div><div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif">Thanks Makoto. This is very helpful. I'd still love to be able to choose some channels though; My P300 comes along with an effect of an opposite (negative) signal in the frontal electrodes (I think this happens because I use the average as a reference), and I believe that choosing just some centro-parietal electrodes would give me a better estimate. I'll see if I can try something myself. </div>
<div>
<div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif"><br></div><div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif">
>-550.46 should be a number of latency. Where does the line (extending from the scalp topos) pointing? Isn't it -550.46 ms? If so, you should limit the latency window to evaluate contribution.</div><div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif">
<br></div></div><div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif">It seems to me that the line is pointing at 559 and what I'm talking about says "pvaf: 550.46. I had limited the latency anyway (from 200 to 600ms). Another example is the cluster close to it, which shows "pvaf: -111.61". This one is also pointing at latency ~559ms. I've attached an example of the output I get from std_envtopo.</div>
<div>
<div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif"><br></div><div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif">
>No, actually pvaf never sums to 100% if you add up each clusters. That means, pvaf(Cls1+Cls2) ~= pvaf(Cls1)+pvaf(Cls2). By the way the default 100% is pvaf(Cls1+Cls2+...ClsN) if you have N number of clusters.</div><div class="gmail_extra" style="color:rgb(34,34,34);font-size:13px;font-family:arial,sans-serif">
The measure pvaf is always superadditive i.e. exceeds 100% if summed separately.</div><div class="gmail_extra" style="color:rgb(34,34,34);font-size:13px;font-family:arial,sans-serif"><br>
</div></div><div class="gmail_extra" style="color:rgb(34,34,34);font-size:13px;font-family:arial,sans-serif">Here I was talking about the individual pvaf (for each individual cluster) shown under the cluster topoplot, and not the total pvaf. I'm not sure if the response is still the same.</div>
<div class="gmail_extra" style="color:rgb(34,34,34);font-size:13px;font-family:arial,sans-serif">For example, for those two clusters that I'm talking about above, with pvaf -550.46% and -111.61%. Would it be possible to get positive values like those for individual clusters? i.e. pvaf: 550.46?</div>
<div class="gmail_extra" style="color:rgb(34,34,34);font-size:13px;font-family:arial,sans-serif"><br></div><div class="gmail_extra" style="color:rgb(34,34,34);font-size:13px;font-family:arial,sans-serif">
Thank you again for your help, Makoto. </div><div class="gmail_extra" style="color:rgb(34,34,34);font-size:13px;font-family:arial,sans-serif"><br></div><div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif">
<br></div><div style="color:rgb(34,34,34);font-size:13.142857551574707px;font-family:arial,sans-serif"><br></div><div><div><div><div><br><div class="gmail_quote">2013/4/10 Makoto Miyakoshi <span dir="ltr"><<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>></span><br>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr">Dear Ana,<div><br></div><div><div><div style="font-family:arial,sans-serif;font-size:13.142857551574707px">
>First, I was wondering wether I could use something like the 'plotchans' (available in envtopo) to compute the contributions in just one or a few channels of interest. The default function computes the contributions for the grand ERPs which gives me a lot of variability that I'm not interested in.</div>
<div style="font-family:arial,sans-serif;font-size:13.142857551574707px"><br></div></div><div style="font-family:arial,sans-serif;font-size:13.142857551574707px">Currently this is not supported. I see how to do it though.</div>
<div style="font-family:arial,sans-serif;font-size:13.142857551574707px">I would suggest that you exclude clusters of non-interest, or specify the clusters to use, so that your outermost envelope is consist of only necessary ones.</div>
<div>
<div style="font-family:arial,sans-serif;font-size:13.142857551574707px"><br></div><div style="font-family:arial,sans-serif;font-size:13.142857551574707px">>Also, I'm still confuse about the pvaf for the individual clusters. </div>
<div style="font-family:arial,sans-serif;font-size:13.142857551574707px">For example, I'm looking for the clusters that explain the P300 variability and there is a cluster (out of 7 clusters) that seems to me (based on the cluster's ERP) that explains most of the variability of the P300. </div>
<div style="font-family:arial,sans-serif;font-size:13.142857551574707px">When I run std_envtopo, I get a pvaf of -550.46 for that cluster. First, I understand that this is not talking about the P300 activity alone, but the grand average, and this implicates a lot of variance from many areas that I'm not interested in (and this is the reason why I'm trying to use just a few channels of interest). Therefore, because what I think is the 'P300 cluster' may have a different signal than all the other activities (that I'm not interested in), then I get a negative value for this cluster pvaf (-550.46). Am I getting something wrong so far?</div>
<div style="font-family:arial,sans-serif;font-size:13.142857551574707px"><br></div></div><div style="font-family:arial,sans-serif;font-size:13.142857551574707px">-550.46 should be a number of latency. Where does the line (extending from the scalp topos) pointing? Isn't it -550.46 ms? If so, you should limit the latency window to evaluate contribution.</div>
<div>
<div style="font-family:arial,sans-serif;font-size:13.142857551574707px"><br></div><div style="font-family:arial,sans-serif;font-size:13.142857551574707px">>Also, I imagine that, because this is the percent variance accounted for, for positive numbers, 100 should be the maximum possible value?<br>
</div></div></div><div class="gmail_extra"><br></div><div class="gmail_extra">No, actually pvaf never sums to 100% if you add up each clusters. That means, pvaf(Cls1+Cls2) ~= pvaf(Cls1)+pvaf(Cls2). By the way the default 100% is pvaf(Cls1+Cls2+...ClsN) if you have N number of clusters.</div>
<div class="gmail_extra"><br></div><div class="gmail_extra">The measure pvaf is always superadditive i.e. exceeds 100% if summed separately.</div><div class="gmail_extra"><br></div><div class="gmail_extra">Makoto<br><br>
<div class="gmail_quote"><div><div>
2013/4/10 Ana Navarro Cebrian <span dir="ltr"><<a href="mailto:anavarrocebrian@gmail.com" target="_blank">anavarrocebrian@gmail.com</a>></span><br>
</div></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div><div><div dir="ltr"><div><span style="border-collapse:collapse;font-family:arial,sans-serif;font-size:13px">Hi,</span><br>
</div>
<span style="border-collapse:collapse;font-family:arial,sans-serif;font-size:13px"><div>
I have two questions about the function std_envtopo.m </div><div><br></div><div>First, I was wondering wether I could use something like the 'plotchans' (available in envtopo) to compute the contributions in just one or a few channels of interest. The default function computes the contributions for the grand ERPs which gives me a lot of variability that I'm not interested in.</div>
<div><br></div><div>Also, I'm still confuse about the pvaf for the individual clusters. </div><div>For example, I'm looking for the clusters that explain the P300 variability and there is a cluster (out of 7 clusters) that seems to me (based on the cluster's ERP) that explains most of the variability of the P300. </div>
<div>When I run std_envtopo, I get a pvaf of -550.46 for that cluster. First, I understand that this is not talking about the P300 activity alone, but the grand average, and this implicates a lot of variance from many areas that I'm not interested in (and this is the reason why I'm trying to use just a few channels of interest). Therefore, because what I think is the 'P300 cluster' may have a different signal than all the other activities (that I'm not interested in), then I get a negative value for this cluster pvaf (-550.46). Am I getting something wrong so far?</div>
<div><br></div><div>Also, I imagine that, because this is the percent variance accounted for, for positive numbers, 100 should be the maximum possible value?</div><div><br></div><div>I hope that makes sense. Thanks in advance for your help.</div>
<span><font color="#888888">
<div>Ana</div></font></span></span></div>
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<div><br></div>
-- <br><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div>
</font></span></div></div>
</blockquote></div><br><br clear="all"><div><br></div></div></div><span><font color="#888888">-- <br>Ana Navarro-Cebrian<br>Postdoctoral Fellow. UCSF
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</blockquote></div><br><br clear="all"><div><br></div>-- <br><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div>
</div></div></div></div>
</blockquote></div></div></div><div class="HOEnZb"><div class="h5"><br><br clear="all"><div><br></div>-- <br>Ana Navarro-Cebrian<br>Postdoctoral Fellow. UCSF
</div></div></blockquote></div><br><br clear="all"><div><br></div>-- <br><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br>
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