Hello, my name is Arnaud Delorme
and I'm the main software architect of the EEGLAB software for processing EEG data.
This is part 4: EEGLAB history and usage statistics.
So EEGLAB's ancestor was created in 1997 at the Salk Institute.
Then in 2001, we had the first version of EEGLAB for artifact rejection.
At that time it was only doing artifact rejection.
And this was at the Salk Institute.
In 2003, we integrated all the tools and we released EEGLAB to a wider audience.
In 2004, we had specific support from NIH to support EEGLAB
and this is the year of the reference paper of EEGLAB.
In 2006, we implemented group analysis as well as a system to add extensions for EEGLAB.
[In] 2009, we have new associated toolboxes, which I'm going to talk about a little bit.
[In] 2011, a paper was published showing EEGLAB was the most popular EEG software for processing EEG.
In 2014, we had the EEGLAB plug-in manager that allowed you to install plugins from within EEGLAB.
In 2018, we had the integration with LIMO to perform linear model of EEG data.
And 2019, that's what we're working on right now, integration with BIDS and big data pipelines.
So EEGLAB is a collection of about 600 functions (70,000 lines of code).
There [are] about 250 thousand downloads over the past 10 years.
We have 6,500 users on the discussion list and 15,000 on the diffusion list
which is for more rare messages.
We have had NIH funding since 2003.
There [are] about 100 plugins right now.
And EEGLAB has been supporting about $288 million of research in the US.
This is by looking at all the papers that you see EEGLAB and totaling their grants.
There [are] about 140,000 EEGLAB sessions per month
from about 10,000 users from all over the world
mostly North America, Europe and Asia.
In 2011, there was a paper by Hanke & Helcencko
[which] showed that EEGLAB was the most used software for processing EEG data.
We also have the EEGLAB reference article, which is above 10,000 citations as of 2019 and seems to still increase.
This is a comparison of EEGLAB with other software packages, both commercial and Fieldtrip
(and of course this is probably biased)
but it lists the different types of license, either open-source or commercial
user base and different capabilities of the software,
EEGLAB you see has [the] most greens, compared to the others.
This might not be totally up to date with respect to each software, but this gives you a rough idea of the field.
We also have a page comparing EEGLAB versus commercial software.
There's a quote from a researcher (M Buchanan) in Nature Physics
that says, "Can we trust the results of a new paper
if they depend on calculations carried out by proprietary software with non-public source code?"
So there was a strong push for using software where the source code is open.
We have a point-by-point comparison with Lending's software packages
where open source might be preferable, and when the commercial software might be preferable.
So I invite you to visit that wiki page.
The strength of EEGLAB is also all the plugins [available].
We've developed many of them ourselves, and I'm going to present a couple of them right now.
SIFT (source information flow toolbox) allows you to calculate brain dynamical activity.
NFT (neuroelectromagnetic forward head modeling toolbox) allows you to do precise source localization of ICA components.
And there is a new extension called EEGLAB and BIDS, [which] allows you to import and export in BIDS format.
There's also ICLabel, which is a new plugin for automated ICA component labeling, so it's very useful in pipeline.
And you can go on the website and classify components, and compare your performance with experts.
There is also, in the reference paper, comparison with other methods
(of course highly biased).
There [are] many other plug-ins for ICA source rejection, that's just the one we developed in lab.
I invite you to look at the other plugins.
To do that, you can use the plugin manager.
There's the online version, and that's the version from within EEGLAB.
So you click on a given plug-in and it gives you the rating, the number of downloads, as well as a description of the plug-in.
There is often also a link to the documentation, if documentation is available.
So in the next lecture we'll start to see how to process data in EEGLAB.