Baris - <div><br></div><div>Below did you leave out a step '6.5 Back-project the remaining ICs to the scalp channel space, then select a channel of interest.' ?</div><div><br></div><div>If so, you may be ignoring the strongest part of ICA-based filtering, namely looking for significant effects at the single IC source level. Of course here one wants then to compare ICs (and their effects) across subjects, for which a great deal of machinery exists in EEGLAB.</div>
<div><br></div><div>Best,</div><div><br></div><div>Scott Makeig</div><div><br></div><div><br><div class="gmail_quote">On Wed, Dec 1, 2010 at 1:23 PM, Baris Demiral <span dir="ltr"><<a href="mailto:demiral.007@googlemail.com">demiral.007@googlemail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">Dear David,<div><br></div><div>Thank you for your e-mail. I should mention that I am enthusiastically following your work on the reliability of the ICA and other EEG related issues. I also plan to use Mass Univariate ERP toolbox sometime in future. </div>
<div><br></div><div>It took me a while to test and play with the data. Well, here is what I have done, and my evaluation:</div><div><br></div><div>I had max 40 trials from each condition. Participants made mean of 6 incorrect decisions per condition, so the odds of answering a question correctly was 34/6. I analyzed the data in this order:</div>
<div><br></div><div>1-Filtered, re-referenced (bi-mastoid), epoched data goes into ICA (epochs with very gross artifacts are removed as suggested by ADJUST algorithm leading to around 1 or at most 2 epochs to be rejected)</div>
<div>2- Baseline to -200-0ms</div><div>3-If correct trials should be used, then select the correct epochs and go to 5, else go to 4</div><div>4-Use all trials</div><div>5- Use ADJUST algorithm to detect and remove problematic ICs automatically</div>
<div>6-Take out the incorrect trials if there are any</div><div>7-Export data for statistics</div><div><br></div><div>I also ran the classical method of rejecting the epochs with over 100 microvolts observed on the EOG electrodes (H1, H2 and VA2) before baselining, leading to the elimination of mean of 6-7 epochs per participant most probably due to the eye blinks. But, note that even though this method is quiet common, it somehow ignores the individual EOG potential difference strengths (some subjects might have eye blinks less than 100 microvolts sometimes).</div>
<div><br></div><div>When I used all the epochs for feeding ICA, the statistical output (ran ANOVA) was almost mirroring the statistical output I obtained from the classical method, and the components which I was suspecting of being artifacts disappeared. When I used the correct trials only, some earlier and later components (mainly centro-temporal components which I believe not artifacts) disappear/attenuated and in one case one new components appeared mainly frontally. </div>
<div><br></div><div>My overall experience suggest that using 40 epochs per condition including the incorrect epochs might be better IF you are only concerned about 'artifact correction' via a toolbox like ADJUST which is mainly depending on the ICAs. Ignoring ICA reliability issues and assuming ADJUST treats the data similarly every time, I think we need to use more trials.</div>
<div><br></div><div>best,</div><div>Baris</div><div><div><div></div><div class="h5"><br><div class="gmail_quote">On Thu, Nov 11, 2010 at 3:33 PM, David Groppe <span dir="ltr"><<a href="mailto:dgroppe@cogsci.ucsd.edu" target="_blank">dgroppe@cogsci.ucsd.edu</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Hi Baris,<br>
ICA's performance will generally degrade as the number of<br>
electrical sources increases (see<br>
<a href="http://www.cogsci.ucsd.edu/~dgroppe/PUBLICATIONS/GroppeCSO2008.pdf" target="_blank">http://www.cogsci.ucsd.edu/~dgroppe/PUBLICATIONS/GroppeCSO2008.pdf</a>).<br>
The incorrect trials probably have some EEG activity not present (or<br>
at least less present) in the correct trials. So if you have<br>
sufficient data to run ICA on just the correct trials, it would<br>
probably be better just to use the correct trials. If you don't have<br>
enough data using just the correct trials though, you'll probably be<br>
fine using the all the trials, since surely a lot of the EEG activity<br>
is common to both sets of trials.<br>
hope this helps,<br>
-David Groppe<br>
<div><br>
<br>
On Wed, Nov 10, 2010 at 4:19 PM, Baris Demiral<br>
<<a href="mailto:demiral.007@googlemail.com" target="_blank">demiral.007@googlemail.com</a>> wrote:<br>
</div><div><div></div><div>> Hi everyone,<br>
> I am running a simple EEG experiment where I measure reaction times and<br>
> accuracies.<br>
> I want to use ICs for artifact removal, and I will report only the correct<br>
> trials (hits).<br>
> So would it be better to use the correct epochs for the ICA to correct for<br>
> the artifacts or is it OK to use all the epochs to detect the artifacts and<br>
> then run the artifact correction (pop_subcomp) followed by deleting the<br>
> incorrect epochs?<br>
> Thanks,<br>
> Baris<br>
><br>
> --<br>
> SB Demiral, PhD.<br>
> Department of Psychology<br>
> 7 George Square<br>
> The University of Edinburgh<br>
> Edinburgh, EH8 9JZ<br>
> UK<br>
> Phone: +44 (0131) 6503063<br>
><br>
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<br>
</div></div><font color="#888888">--<br>
David Groppe, Ph.D.<br>
<a href="mailto:dgroppe@cogsci.ucsd.edu" target="_blank">dgroppe@cogsci.ucsd.edu</a><br>
<a href="http://www.cogsci.ucsd.edu/~dgroppe/" target="_blank">http://www.cogsci.ucsd.edu/~dgroppe/</a><br>
</font></blockquote></div><br><br clear="all"><br></div></div>-- <br><div class="im">SB Demiral, PhD.<br>Department of Psychology <br>7 George Square<br>The University of Edinburgh<br>Edinburgh, EH8 9JZ<br>UK<br>Phone: +44 (0131) 6503063<br>
</div></div>
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For digest mode, send an email with the subject "set digest mime" to <a href="mailto:eeglablist-request@sccn.ucsd.edu">eeglablist-request@sccn.ucsd.edu</a><br></blockquote></div><br><br clear="all"><br>-- <br>Scott Makeig, Research Scientist and Director, Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla CA 92093-0559, <a href="http://sccn.ucsd.edu/~scott" target="_blank">http://sccn.ucsd.edu/~scott</a><br>
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