<div dir="ltr">Many thanks for your responses, Makoto and Mikolaj!<div style>They were very helpful.</div><div style><br></div><div style>Best regards,</div><div style>Caro</div></div><div class="gmail_extra"><br><br><div class="gmail_quote">
2013/9/11 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:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div dir="ltr">Dear Caro,<div class="im"><div><br></div><div>> I should mention that we had to reconstruct two channels before the analysis due to equipment problems. Could that account for part of the problem? <br></div>
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<br></div></div><div class="gmail_extra">Yes that's it.</div><div class="im"><div class="gmail_extra"><br></div><div class="gmail_extra">> I must also insist on the fact that even when chennels were reconstructed for other subjects, I son-t always get the message.<br>
</div><div class="gmail_extra"><br></div></div><div class="gmail_extra">Because it also depends on data quality. From my experience I would expect that if you have a lot of EMGs you would see that message more often.</div>
<div class="im"><div class="gmail_extra">
<br></div><div class="gmail_extra">> A second question I have is, should I edit the rank to 30 or should I just run it with 28 components? how would that affect my data?</div><div class="gmail_extra"><br></div></div><div class="gmail_extra">
Just run it with 28 components. Using PCA should be fine after channel interpolation, but why don't you go simple if you can.</div><div class="gmail_extra"><br></div><div class="gmail_extra">I have seen and experienced rank issues. I would avoid it whenever I can. Try matlab function rank on your fully processed data. It often returns much less number than your number of channels. <br>
</div><div class="gmail_extra"><br></div><div class="gmail_extra">Makoto</div><div class="gmail_extra"><br></div><div class="gmail_extra"><br><div class="gmail_quote">2013/9/11 Caro Gattei <span dir="ltr"><<a href="mailto:carogattei@gmail.com" target="_blank">carogattei@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 class="h5"><div dir="ltr">Dear all,<div><br></div>
<div>I am currently running an experiment with a 32 channel equipment (30+ ext) and when I try to run ICA in specific subjects I get a message saying that "EEGLAB has detected that the rank of your data matrix is lower the number of input data channels. This might be because you are including a reference channel or because you are running a second ICA decomposition. The proposed dimension for ICA is 28 (out of 30 channels) rana computation may be innacurate so you may edit this number below. If you do not understand, simply press OK"<br>
</div><div><br></div><div>I found an explanation for the error message on the eegab list that said that the ranking could be reduced de to low-pass filtering (being that I do not have a reference channel and hand't run ICA previously:</div>
<div> <a href="http://sccn.ucsd.edu/pipermail/eeglablist/2012/005110.html" target="_blank">http://sccn.ucsd.edu/pipermail/eeglablist/2012/005110.html</a></div><div><br></div><div>However, I get the same message when I try to run ICA with raw data.</div>
<div><br></div><div>I should mention that we had to reconstruct two channels before the analysis due to equipment problems. Could that account for part of the problem? </div><div><br></div><div>I must also insist on the fact that even when chennels were reconstructed for other subjects, I son-t always get the message.</div>
<div><br></div><div>A second question I have is, should I edit the rank to 30 or should I just run it with 28 components? how would that affect my data?</div><div><br></div><div>Best regards,</div>
<div><br></div><div>Caro</div></div>
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-- <br><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div>
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