Dear Kristina and Matt,<div><br></div><div>There is no problem. The subtracted IC1, 7, 11 contained high frequency activity. That's all.</div><div> </div><div>Plot component activations and study how IC1, 7, 11 look like. At least one of them is not very smooth in high-frequency range, right?</div>
<div><br></div><div>What Matt is arguing is another thing that can rise from ill dataset and therefore not necessarily common. Let's confirm with most probable cases first. Thank you though.</div><div><br></div><div>
Makoto<div>
<br><div class="gmail_quote">2012/12/20 Matt Craddock <span dir="ltr"><<a href="mailto:matt.craddock@uni-leipzig.de" target="_blank">matt.craddock@uni-leipzig.de</a>></span><br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Hi Kristina,<br>
<br>
I've recently hit on similar behaviour (and in some cases even *worse*)<br>
with some of my datasets. For me, this seemed to be because of<br>
decomposing the data as if it were full rank (i.e. all the channels are<br>
approx linearly independent from each other) when it wasn't (there are<br>
also previous posts along these lines by Maximilien Chaumon - see<br>
<a href="http://sccn.ucsd.edu/pipermail/eeglablist/2011/004326.html" target="_blank">http://sccn.ucsd.edu/pipermail/eeglablist/2011/004326.html</a>). Looking in<br>
your specific case, I'd be quite cautious about removing those "noisy"<br>
components because they look to me like they also contain some genuine<br>
brain activity (though note that the two are basically identical, just<br>
opposite sign, which makes it look like the same problem as above).<br>
<br>
There are much more expert people than me on this list who can tell you<br>
much better why these noisy components happen, but what I can tell you<br>
is interpolation and rereferencing reduce the rank of the data, and this<br>
can, occasionally, make ICA do things like you're seeing. I'd suggest<br>
running PCA first to reduce the dimensions in the data accordingly. I've<br>
seen Arnaud Delorme suggest on another list you should avoid using PCA<br>
first, but I'm not sure that advice applies when the data is not full-rank.<br>
<br>
Now, if you run ICA through the menus in EEGLAB, it should detect that<br>
your data is not full rank, suggest an appropriate number of components<br>
to return, and then run PCA before ICA. However, if you run the ICA as<br>
part of a script, it'll probably be set up in such a way that it skips<br>
this step of suggesting an appropriate number of components and asking<br>
if it should reduce the data first. You may see a message along the<br>
lines of "fixing rank computation inconsistency probably because you're<br>
on linux 64 bit matlab"; this appears even if you're on Windows, and<br>
always selects the higher number of components returned by two different<br>
methods of calculating rank, which, in my experience, means it always<br>
decomposes the data as if it were full rank. So I'd suggest, if you're<br>
running ICA as part of your script, calculating the rank of your data<br>
and then passing that to the pop_runica function yourself.<br>
<br>
Cheers,<br>
Matt<br>
<div class="im"><br>
On 19/12/2012 10:26, Kristina Borgström wrote:<br>
><br>
>> Hi,<br>
>> I have an issue regarding ICA for artifact correction that I really<br>
>> would appreciate some help with.<br>
>> Here is some background information: I have recorded child data (2<br>
>> years old) with EGI, 128 channels. Before export to EEGLab, the data<br>
>> has been band-pass filtered 1-30 Hz, epoched, clearly bad epochs (with<br>
>> more artifacts than just eye artifacts) were removed, and bad channels<br>
>> in the remaining epochs were interpolated. The data was rereferenced<br>
>> to average mastoid reference. I then imported into EEGlab, which<br>
>> treats the data as continuous, but has all the event information. I<br>
>> have then performed ICA in order to correct for eye artifacts.<br>
>><br>
><br>
</div>> *The problem (please see the image files located at following links):*<br>
>> **<a href="https://dl.dropbox.com/u/7016081/DataPlots.jpg" target="_blank">https://dl.dropbox.com/u/7016081/DataPlots.jpg</a><br>
<div class="im">><br>
> <a href="https://dl.dropbox.com/u/7016081/ICAComponents.jpg" target="_blank">https://dl.dropbox.com/u/7016081/ICAComponents.jpg</a><br>
>><br>
>> In (at least) two data files, besides some clear eye artifact<br>
>> components (components 1 & 11: blink; component 7: horizontal eye<br>
>> movement), the ICA also found two components that look like pure high<br>
>> frequency noise (components 2 & 3).<br>
>> When I remove the eye artifact components (1,7 & 11), the eye<br>
>> artifacts are in fact removed, BUT the data looks generally “noisier”,<br>
>> i.e. the channels overall are fuzzier (see the image “DataPlots” for<br>
>> comparisons). When I calculated individual averages with this data, it<br>
>> indeed contained massive amounts of high frequency noise that was not<br>
>> present in the averages where I did not use ICA at all but instead<br>
>> removed all epochs containing eye artifacts.<br>
>> I then continued and tested removing the “noise components” (2 & 3),<br>
>> and the data then looked like it did originally, minus the eye<br>
>> artifacts. It didn’t seem to have a major effect on the ERP components<br>
>> either, but of course removed the high frequency noise.<br>
>><br>
</div>>> *My main questions are:*How can noise be “added” to the data, after<br>
<div class="im">>> removal of certain components? How can I determine what type of noise<br>
>> components 2 & 3 consist of? I’ve looked at the frequency plots, but I<br>
>> don’t think it’s very clear. Can it be line noise, or EMG? The scalp<br>
>> topographies are very widespread, and EMG is usually more laterally<br>
>> located. Should it be ok to just remove these two components when they<br>
>> appear, or is there a risk that they contain cognitive components?<br>
>> Many thanks for any input you can give me!<br>
>> Regards,<br>
>> Kristina Borgström<br>
>> PhD Student<br>
>> Department of Psychology<br>
>> Lund University<br>
>> Sweden<br>
>> <a href="tel:%2B46-46-2223638" value="+46462223638">+46-46-2223638</a><br>
<br>
</div>--<br>
Dr. Matt Craddock<br>
<br>
Post-doctoral researcher,<br>
Institute of Psychology,<br>
University of Leipzig,<br>
Seeburgstr. 14-20,<br>
04103 Leipzig, Germany<br>
Phone: <a href="tel:%2B49%20341%20973%2095%2044" value="+493419739544">+49 341 973 95 44</a><br>
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</blockquote></div><br><br clear="all"><div><br></div>-- <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>
</div></div>