<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Regarding the matrix rank, we have recently realized that the rank function (and other rank function we had programmed) are not fully reliable which is probably with Max observes the component he observes. The runica function should automatically decrease the rank of the input data matrix. However, sometimes it does not use the correct rank. We have modified the runica GUI so that if the matrix is not full rank, it now pops up a new window suggesting to the user a rank reduced value. This value may be adjusted by the user based on prior knowledge. For instance, if you have removed 5 components from the data, you would reduce the rank by 5 (and overwrite the rank that is automatically detected if it is not correct).<div><br></div><div>Arno<div><br><div><div>On Aug 23, 2011, at 10:11 PM, John J.B. Allen wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">Max<div><br></div><div>I have observed that when the data are not full rank. You can test the rank of your data by reshaping your epoched data to a 2D matrix, and running the rank command, like this:</div><div><br></div>
<div>rank(reshape(EEG.data,EEG.nbchan,EEG.trials*EEG.pnts))</div><div><br></div><div>When I did this, your rank is 63, but you have 69 channels, indicating that some channels are linearly dependent on others. I think this is the source of your problem, and if you remove those channels before running ICA, you should no longer see this issue.</div>
<div><br></div><div>Best</div><div><br></div><div>John</div><div><br></div><div><br></div><div><br><br><div class="gmail_quote">On Tue, Aug 23, 2011 at 07:24, Maximilien Chaumon <span dir="ltr"><<a href="mailto:maximilien.chaumon@gmail.com">maximilien.chaumon@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">Hi eeglabbers,<br><br>I sometimes get ICs with extremely similar topographies and time courses, like on <a href="http://oszilla.hgs.hu-berlin.de/public/Similar_ICs.PNG" target="_blank">this slide</a>.<br>
I know that ICA returns independent components.<br>
Does that not mean that they should not look the same?<br>I know the components are independent in a statistical sense, which is not
the same as uncorrelated, but still. I'm a bit surprised. What do these two components mean if they cancel one another? well, do they?<br><br>Sorry if my question is naive, but what is happening?<br><br>The data is <a href="http://oszilla.hgs.hu-berlin.de/public/Similar_ICs.mat" target="_blank">here</a>.<br>
<br>Best,<br>Max<br>
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