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

Sat Apr 25 01:18:11 PDT 2026

Hi Makoto,

Thank you for sharing this! I agree, the effect shown is indeed dramatic
and very impressive.
However, the demonstration you mentioned [18:13] actually shows the removal
of a tACS (transcranial alternating current stimulation) artifact, which is
delivered continuously in order to recover the underlying EEG signal. It
does not show the removal of artifacts associated with transcranial
magnetic stimulation (TMS) during EEG, which typically involves
single-stimulus trial protocols to study TMS-evoked potentials. Artifact
removal in TMS-EEG experiments is notoriously complex. Aligning with
Giovanni's curiosity, I wonder if anyone is currently testing GEDAI for
this specific application?

Giorgio Leodori, MD, PhD

Department of Human Neurosciences
Sapienza, University of Rome
Viale dell'Università, 30
00185 Roma
giorgio.leodori at uniroma1.it

Il ven 24 apr 2026, 19:17 Makoto Miyakoshi via eeglablist <
eeglablist at sccn.ucsd.edu> ha scritto:

> 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
> > > >> _______________________________________________
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> > > >>
> > > >
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> >
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