[Eeglablist] The ICLabel Website -- A New Education Feature

[Luca Pion-Tonachini]

The ICLabel Website -- A New Education Feature

I want to update everyone on a new education feature available at the 
ICLabel website <reaching.ucsd.edu:8000> (reaching.ucsd.edu:8000). The 
site is designed to collect 'crowdsourced' labels for EEG independent 
components (ICs), based on their displayed properties. I am using the 
submitted labels to train an automated IC classifier However, at the 
recent EEGLAB workshop the site also gained a fair bit of favorable 
response as a useful educational tool using the new option described below.

*For new users wanting to learn the basics about classifying EEG ICs 
based on their measured properties: * I have created a Label with 
feedback <reaching.ucsd.edu:8000/labelfeedback> option. This page is 
almost exactly the original labeling page, but with three key differences:

 1. Feedback is provided after each IC label submission.
 2. Submitted labels are not saved in the classification database.
 3. No login is required to reach the page.

The feedback provided to users is a table of all other labels for that 
IC given by other people. Each label is marked as either by a "user" who 
has signed up through the website or by an "expert" (e.g., a high-skill 
user known to me). To clarify: "user" here does not mean "worse than 
expert", but simply a volunteer label contributor whose skill level is 
unknown.

There is still a Tutorial <reaching.ucsd.edu:8000/tutorial> section 
giving sample classifications with text explanations. The Label with 
feedback <reaching.ucsd.edu:8000/labelfeedback> feature is suitable for 
learning and/or teaching by any researcher or research student who wants 
to learn more about applying ICA decomposition to EEG or related data.

*For (self-selected) contributors to the crowdsourced label database:* A 
second new feature is the user Profile page that now appears by default 
following login. It currently does not have very much information on 
your previous contributions, but that will change with time -- I plan to 
incorporate the output of a crowd-labelling algorithm that estimates 
users' skill by comparing submitted labels with those of established 
'expert' users (i.e. those whose labels are most consistent with other 
skilled contributors). If there is anything else you would find useful 
to see there, do not hesitate to tell me, either by replying to this 
email or by leaving a comment on the website.

I hope you all find the new Label with feedback 
<reaching.ucsd.edu:8000/labelfeedback> page to be helpful and, after 
using it, some more of you will choose to become contributors who submit 
more IC labels to help the classification algorithm become more accurate.
Finally, I have some working prototypes of the classifier and plan to 
post a sort of progress report on the website, but I will leave that for 
a future email.

Sincerely,
Luca Pion-Tonachini


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