[Eeglablist] Let's test whether GEDAI is a post-ASR EEG artifact rejection champion

[Vikram Shenoy Handiru]

Hello all,

First of all, thank you Makoto for kickstarting this discussion about GEDAI
:)

I routinely use GEDAI in my analyses (includes TMS-EEG, EEG during walking,
EEG during balance perturbation, etc.) and I must say GEDAI-preprocessed
data looks pretty good.  For context - I've used ASR for several years now,
and it was a core part of my preprocessing steps. Having compared the
results of ASR and GEDAI, I'm inclined to say GEDAI appears to be better.

This is my usual pipeline (that has worked for most datasets):

Data acquisition: Brain products ActiChamp (64-ch)+ Brainsight
Neuronavigation for 3D digitization of electrode positions+MRI (whenever
available) - my research involves EEG source reconstruction.

*Preprocessing steps:*
* Bad channel removal + interpolation (pop_rejchan + pop_interp)
* Zapline for line noise (60 Hz) removal  (I found this to be better than
cleanline).
* bandpass filtering (0.5 Hz-50 Hz)
* GEDAI on the continuous data (usually, with auto or auto- setting,
depending on the nature of data recording). Caution: enabling parallel
processing option can be counterproductive for long recordings in my
experience.
* ICA (cudaica implementation of extended infomax)
* IClabel to automatically label the components and filter out the
artifacts. I usually keep relatively conservative settings: only ICs with
brain probability > 0.5 are retained.

I have not tried custom BEM on our datasets yet, as I was satisfied with
the default model. If anyone can confirm whether custom lead fields help,
I'd give it a try (we already have MRI+3D digitized positions for some
participants).

Can anyone please suggest whether this pipeline looks okay overall or can
it be further optimized?

Thanks and Regards,

*Vikram Shenoy Handiru, PhD*

Research Scientist and Director - NeuroMuscular and Electrophysiology
Laboratory (NMEL)
Center for Mobility and Rehabilitation Engineering Research

Kessler Foundation  | 1199 Pleasant Valley Way, West Orange, NJ 07052

*T.* +1.973.324.3578 |* F*. +1.973.324.3527 | VShenoy at KesslerFoundation.org

*Affiliation:* Research Assistant Professor,

Department of Physical Medicine & Rehabilitation | Rutgers, New Jersey
Medical School



On Mon, Mar 30, 2026, 18:09 Makoto Miyakoshi via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Hi EEGLAB mailing list subscribers,
>
> I watched this Youtube video on GEDAI, a relatively new EEG artifact
> rejection algorithm, and got very impressed. If you are interested in, or
> looking for, an effective artifact rejection method, you definitely want to
> check it out.
>
> https://urldefense.com/v3/__https://www.youtube.com/watch?v=qSM5narynzc__;!!Mih3wA!CLlPjuH-5y0g4OtnG1F8KKjQoNScMgDneZVoRYZ1yoUGb_ySMhoAaf4jO0oLiDxE_IZRlBbTXuSFkSA4XgNw9TOfbZ8$
> Do not miss the scene in which Tomas shows a comparison before and after
> removing the TMS-induced artifact (about 20 min position). I've never seen
> a more dramatic demo than this. It is only comparable to successful demo of
> gradient artifact removal from fMRI-EEG recording.
>
> Here is a suggestion to this community:
> If you have been already using ASR, why don't you please use GEDAI as well
> in parallel, compare the results, and share your impression with us? I'll
> do so in my next project and report it here.
>
> The reason why I like GEDAI is that the concept of combining lead field +
> GED is elegant. The idea of 'learning from data's own cleanest part' is the
> same as ASR, but GEDAI does it more explicitly and simply. GEDAI is like
> ASR + REST (a reference method using a forward model).
>
> Makoto
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