<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body dir="auto"><div></div><div><div><font color="#000000"><span style="background-color: rgba(255, 255, 255, 0);">Dear EEGLAB mailing list subscribers,</span></font></div></div><div><font color="#000000"><span style="background-color: rgba(255, 255, 255, 0);"><br></span></font></div><div>A week ago I introduced a website for labeling EEG independent components and asked for your help in doing so. Unfortunately the website was broken at the time of my last email due to a last minute change. I've since fixed that problem and invite you all to try it again. You can find more information at <a href="http://reaching.ucsd.edu">reaching.ucsd.edu</a>:8000/tutorial or by reading the original email copied below.</div><div><br></div><div>Thank you,</div><div>Luca Pion-Tonachini </div><div><br>On Feb 5, 2016, at 9:15 AM, Luca Pion-Tonachini <<a href="mailto:lpionton@ucsd.edu">lpionton@ucsd.edu</a>> wrote:<br><br></div><blockquote type="cite"><div>
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Dear EEGLAB mailing list subscribers,<br>
<br>
I would like to ask for your help on a project that will likely be
of use to you in your research or any other endeavor involving ICA
decomposition of EEG data. I am an Electrical Engineering Ph.D.
student at UCSD and I work at the Swartz Center for Computational
Neuroscience, where EEGLAB is developed.<br>
<br>
Here at the SCCN, we have gathered a lot of ICA decomposed EEG data.
My plan is to use it to create a multi-class EEG independent
component (IC) classifier, one you can run and trust the results as
much as if a domain expert had personally labeled them for you. The
secret to making such a classifier accurate is in the data used to
train it. Including results from many different experiments,
electrode montages, subjects, and EEG devices — as well as tentative
classifications by many judges — should allow a level of
generalization that makes the resulting classifier usable on any
dataset. A lot of people I’ve spoken with are excited by this
prospect, as I hope you are too.<br>
<br>
If you are interested in participating in this project, please
browse <a href="reaching.ucsd.edu:8000/tutorial">this website</a> (
<a class="moz-txt-link-freetext" href="http://reaching.ucsd.edu:8000">http://reaching.ucsd.edu:8000</a> ) and suggest classifications for as
many ICs as you have time for. Each IC is represented by a figure
showing several measures (scalp map, equivalent dipole location,
mean spectrum, erpimage, etc.). There is a tutorial on the site that
will tell you more about the project and how to use it — and also a
guide to discriminating several types of EEG IC processes. <br>
<br>
If you have comments, questions, or suggestions, please give me
feedback through the website or at this email address. We will make
use of all data entries, regardless of how many get labeled — and
each IC you label will make the classifier more useful.<br>
<br>
Thank you,<br>
Luca Pion-Tonachini
</div></blockquote><blockquote type="cite"><div><span>_______________________________________________</span><br><span>Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a></span><br><span>To unsubscribe, send an empty email to <a href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu">eeglablist-unsubscribe@sccn.ucsd.edu</a></span><br><span>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></span></div></blockquote></body></html>