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

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Wed Apr 29 11:28:37 PDT 2026 

Hi Giorgio and Ros,

> To be clear the artifact in the video is during tACS stimulation.

> 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.

Sorry, I was wrong. The demo was using tACS, your are right.

As a gesture of apology, let me share this info: to reduce stimulation
artifacts in simultaneous TMS-EEG, it is necessary to optimize directions
of the leads of electrodes so that they are as parallel to the magnetic
field line as possible. The effect is substantial. In this paper written by
a Japanese team, you can see quantitative comparison between with and
without this optimization.

https://urldefense.com/v3/__https://doi.org/10.1016/j.clinph.2010.09.004__;!!Mih3wA!ETW41EbIly-g20wyX1GxsumJ3foOTzJR-OBCplgNSJyUntrFF1K3Ltvh5VqoM5MNkBHBtImnT47mKdaZ4ITCcznMp-Y$ 

If electrode leads are well optimized, the artifact duration in the time
domain will be nicely limited to only a few millisecond. Then you can
address them with a simpler solution, such as ARfit-based time-domain
interpolation (for example, https://urldefense.com/v3/__https://eeglab.org/plugins/ARfitStudio/__;!!Mih3wA!ETW41EbIly-g20wyX1GxsumJ3foOTzJR-OBCplgNSJyUntrFF1K3Ltvh5VqoM5MNkBHBtImnT47mKdaZ4ITCz4Uu81Y$ ).
Problems happen then the artifact show random-length tails which could be
up to 30-50 ms, such a simple cosmetic fix no longer works (but GEDAI may
still work in principle).

Makoto


On Sat, Apr 25, 2026 at 4:18 AM Giorgio Leodori <giorgio.leodori at uniroma1.it>
wrote:

> 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
>> > > >> _______________________________________________
>> > > >> To unsubscribe, send an empty email to
>> > > >> eeglablist-unsubscribe at sccn.ucsd.edu or visit
>> > > >> https://sccn.ucsd.edu/mailman/listinfo/eeglablist    .
>> > > >>
>> > > >
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>> >
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