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

Sun Apr 19 05:12:10 PDT 2026

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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