[Eeglablist] Announcing GEDAI: A new EEGLAB Plugin for unsupervised artifact correction

[Alejandro Ojeda]

Hi Tomas,

Your method seems to be very similar to an earlier one proposed here:
https://urldefense.com/v3/__https://doi.org/10.1162/neco_a_01415__;!!Mih3wA!H_aYzLvzhjkSGle4cUl8-F0NuyhKgbVd9MZLP9H6UOmD0cfWKsnxgydDcbO1fcidugThUPVbNxSCBR3PSwu_T5Gh8SKi$ 
There is also code that plays within EEGLAB here:
https://urldefense.com/v3/__https://github.com/aojeda/dsi/tree/master__;!!Mih3wA!H_aYzLvzhjkSGle4cUl8-F0NuyhKgbVd9MZLP9H6UOmD0cfWKsnxgydDcbO1fcidugThUPVbNxSCBR3PSwu_TwmFtgr-$  (currently unmaintained by me but
last time I checked nothing was broken).
Perhaps for the peer-reviewed version it is worthwhile to comment on their
differences (or not, just a random idea ;-)).

Best,
Alejandro


On Sat, Nov 8, 2025 at 7:50 PM Makoto Miyakoshi via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Hi Tomas,
>
> Using a forward model in defining 'clean' brain signals is a great idea.
> The idea reminds me of REST by Dezhong Yao.
> Though I still need to read details, intuitively I think this could be a
> better way to go than ASR.
>
> Makoto
>
> On Mon, Oct 20, 2025 at 8:59 PM Tomas Ros via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
> > Dear EEGLAB community,
> > We are writing to announce our new plugin, the Generalized Eigenvalue
> > De-Artifacting Instrument (GEDAI), which is now available in the
> Extension
> > Manager.
> > GEDAI was developed to provide robust and rapid artifact removal, even
> for
> > highly contaminated data, without requiring user supervision. It
> introduces
> > a novel approach called "leadfield filtering," which uses a theoretical
> EEG
> > forward model to separate brain activity from artifacts.
> > GEDAI was designed to address key limitations of current ICA- or PCA-
> > based denoising, such as speed, challenging artifact mixtures, or need
> for
> > calibration data.
> > Our benchmark tests<
> >
> https://urldefense.com/v3/__https://www.biorxiv.org/content/10.1101/2025.10.04.680449v1__;!!Mih3wA!AUK0DyQeIgIlJl7UGbrzSjRIorBGG-rXomeQ_Z4PbNBQiWBRFkCrHDO-8gYgLImNW83Yh6iO78ayvq0yjDaFEJxo2w$
> > > using real-world data indicate that GEDAI preprocessing can result in
> > more accurate neurobehavioral prediction compared to competing methods.
> > Nevertheless, it has not been tested under all EEG scenarios and we would
> > welcome both positive and negative feedback on its performance.
> > You can also download the plugin and find its documentation here:
> >
> https://urldefense.com/v3/__https://neurotuning.github.io/GEDAI-master/__;!!Mih3wA!AUK0DyQeIgIlJl7UGbrzSjRIorBGG-rXomeQ_Z4PbNBQiWBRFkCrHDO-8gYgLImNW83Yh6iO78ayvq0yjDYaj2FnGg$
> > <
> >
> https://urldefense.com/v3/__https://github.com/neurotuning/GEDAI-master__;!!Mih3wA!AUK0DyQeIgIlJl7UGbrzSjRIorBGG-rXomeQ_Z4PbNBQiWBRFkCrHDO-8gYgLImNW83Yh6iO78ayvq0yjDYf-A47VA$
> > >
> > We hope GEDAI becomes a valuable tool for the community.
> > Best regards,
> > the GEDAI development team
> >
> > ▬▬▬
> >
> >
> > Tomas Ros, PhD
> >
> > Lecturer, Department of Clinical Neurosciences
> >
> > Research Staff Scientist, CIBM EEG HUG-UNIGE Section
> > University of Geneva, Switzerland
> >
> >
> https://urldefense.com/v3/__https://www.tomasros.com/__;!!Mih3wA!AUK0DyQeIgIlJl7UGbrzSjRIorBGG-rXomeQ_Z4PbNBQiWBRFkCrHDO-8gYgLImNW83Yh6iO78ayvq0yjDZ-36KFHg$
> >
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> >
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> >
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-- 
Alejandro Ojeda, Ph.D.

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