[Eeglablist] Independent component classification help from eeglablist members

[Luca Pion-Tonachini]

Dear EEGLAB mailing list subscribers,

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.

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.

If you are interested in participating in this project, please browse 
this website <reaching.ucsd.edu:8000/tutorial> ( 
http://reaching.ucsd.edu:8000 ) 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.

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.

Thank you,
Luca Pion-Tonachini
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