[Eeglablist] ICLabel: paper online

[Scott Makeig]

Luca Pion-Tonachini's successful defense of his dissertation recently
included presentation of his successful project to build a machine-learning
based EEG independent component (IC) classifier with several novel and
useful properties. A paper on ICLabel has now been published in NeuroImage
- follow this authors' link <https://authors.elsevier.com/c/1Z6AA3lc~r73Nv>
to download free copies.

The ICLabel website <https://labeling.ucsd.edu/tutorial/>
(*labeling.ucsd.edu/tutorial/
<http://labeling.ucsd.edu/tutorial/>*) continues to be a valuable resource
for learning to appreciate differences in spatial and temporal properties
of ICs accounting for brain, eye, muscle, line noise, and other effective
EEG sources. (Note: the site is quite usable for EEG course assignments).

The ICLabel website also continues to accept ('crowd-sourced') help from
users who wish to contribute labels for the still more than 100,000 ICs on
the SCCN servers that yet have no suggested labels... As new label
suggestions accumulate, the ICLabel algorithm will be regularly retrained
on the growing set of labeled ICs -- thus new label suggestions will
contribute to further improving the robustness of the ICLabel plug-in,
which is now already in use in many EEG labs and analysis pipelines around
the world.

The website also has a leading contributor 'leaderboard,' and your label
suggestions can receive a (private) 'Compared to Experts' estimate for your
information ...

Scott Makeig
for the EEGLAB team and Luca Pion-Tonachini

Scott


-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0961, http://sccn.ucsd.edu/~scott