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

Fri Apr 24 10:43:37 PDT 2026

Hiya,

To be clear the artifact in the video is during tACS stimulation. But it
works in the same way with TMS artifacts.

Cheers,
Tomas

▬▬▬

*Tomas Ros, PhD*

Lecturer, Department of Clinical Neurosciences
CIBM EEG HUG-UNIGE Section
University of Geneva, Switzerland
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On Fri, 24 Apr 2026, 18:53 Makoto Miyakoshi via eeglablist, <
eeglablist at sccn.ucsd.edu> wrote:

> Hi Giovanni,
>
> My first post referred to the TMS-induced artifact. See below.
>
> 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!HTKTVxly3pDUSyrLZmJ3xcPTz5jRKU9tf6h4P8_LoApdHAyqXopR0ONp7Y5k5N4Oh56cPVU2AR24ijqfimK9M7-M_Sk$
> 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.
>
> Makoto
>
> On Sun, Apr 19, 2026 at 8:12 AM Giovanni Pellegrino <
> giovannipellegrino at gmail.com> wrote:
>
> > Hello everyone,
> >
> > Does anyone have experience using GEDAI to clean TMS-EEG data? I am
> > especially interested in whether it can complement or partly
> > substitute for TESA in a standard pipeline.
> > Any practical advice or references would be much appreciated.
> >
> > Thanks very much.
> >
> > Giovanni
> >
> > Giovanni Pellegrino, MD, PhD, FACNS
> > Neurologist - Epileptologist
> >
> > On Mon, Apr 6, 2026 at 6:19 PM Makoto Miyakoshi via eeglablist
> > <eeglablist at sccn.ucsd.edu> wrote:
> > >
> > > Thank you Vikram for sharing your experience and opinion.
> > >
> > > > I've used ASR for several years now, and it was a core part of my
> > > preprocessing steps.
> > >
> > > May I ask parameters, particularly the k value (i.e., cutoff threshold
> in
> > > SD)?
> > >
> > > > 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.
> > >
> > > It is possible that after GEDAI you don't use IC rejection and you can
> > > retain more (non-ICA-decomposed) brain signals, particularly those with
> > low
> > > amplitudes.
> > >
> > > Castellanos NP, Makarov VA. (2006).  Recovering EEG brain signals:
> > artifact
> > > suppression with wavelet enhanced independent component analysis. J
> > > Neurosci Methods. 2006 Dec 15; 158(2) 300-312
> > > DOI: 10.1016/j.jneumeth.2006.05.033, PMID: 16828877
> > >
> > > > Can anyone please suggest whether this pipeline looks okay overall or
> > can
> > > it be further optimized?
> > >
> > > You should band-pass filter the data at the beginning of your process
> so
> > > that you do not need to use ZapLine. It should also help to perform
> your
> > > bad channel removal (unless your recording is already
> > > well-high-pass-filtered at 0.5-1.0 Hz, which is unlikely anyway) My
> > > suggestion is:
> > >
> > > * bandpass filtering (0.5 Hz-50 Hz)
> > > * Bad channel removal
> > > * GEDAI on the continuous data
> > > * Bad channel interpolation (to prevent effective rand deficiency at
> the
> > > previous GEDAI stage; see my ICA's bug paper)
> > > * ICA + * IClabel (NOT for component rejection)
> > >
> > > > 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).
> > >
> > > In the case of REST, it is not (very) sensitive to the choice of the
> > > forward model, if I remember correctly.
> > > Even if you do your best with MRI-derived morphometry + digitized
> > electrode
> > > locations, there are (at least) two other invincible limitations:
> tissue
> > > conductivity and presence of parietal (and possibly other) foramens.
> > >
> >
> https://urldefense.com/v3/__https://en.wikipedia.org/wiki/Parietal_foramina__;!!Mih3wA!DWnnv7lCFyiURCxsJ9d6iuSeSBc8ex9Vn0NKqDin52tAOevJn7ZJxJ_qcH-xoqKZuDO8dfH5CzPtz-hQkACt-98WZdc$
> > I think the issue of
> > > foramen alone is sufficient to make me pessimistic about obtaining an
> > > accurate EEG forward model, not only because it is technically
> difficult
> > to
> > > address but also because no one knows it.
> > >
> > > Makoto
> > >
> > >
> > > On Thu, Apr 2, 2026 at 1:03 PM Vikram Shenoy Handiru <
> > vikkyshenoy at gmail.com>
> > > wrote:
> > >
> > > > 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
> > > >> _______________________________________________
> > > >> To unsubscribe, send an empty email to
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> > > >> https://sccn.ucsd.edu/mailman/listinfo/eeglablist    .
> > > >>
> > > >
> > > _______________________________________________
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> >
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