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

Thu Apr 2 14:51:38 PDT 2026

Hi Thomas,

This is great, thank you for answering all my questions. Great ressource to have!

Quick comment, maybe to throw a warning for useers with low-density montages: while it may work, your algorithm automatically applies CAR to the signals in the process (well-explained, thank you), which be a serious problem with low-density sparse montages.

Cedric

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On Thursday, April 2nd, 2026 at 5:22 AM, Ros, Tomas <dr.t.ros at gmail.com> wrote:

> Dear Cedric, dear all,
>
> I am very happy to take any questions regarding GEDAI and welcome your feedback to further improve the plugin.
>
> To this end, I have just finished creating a GEDAI FAQ (Frequently Asked Questions): https://urldefense.com/v3/__https://github.com/neurotuning/GEDAI-master/wiki/Frequently-Asked-Questions-(FAQ)__;!!Mih3wA!GjU65acT1MKkz7AvcEmNV7wYyMfIcEGjKZkbXPto3wp7Iv7XPWYwJO-33OcLrmJ-xmPLrpVxqZ396WUumwQoJinVtw$ 
>
> This can always be updated with new info :)
>
> Thank you for taking GEDAI for a spin!
> Tomas
>
> ▬▬▬
>
> Tomas Ros, PhD
>
> Lecturer, Department of Clinical Neurosciences
>
> CIBM EEG HUG-UNIGE Section
> University of Geneva, Switzerland
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> On Wed, 1 Apr 2026 at 23:53, Cedric Cannard via eeglablist <eeglablist at sccn.ucsd.edu> wrote:
>
>> Hi Rob,
>>
>> If you see the signal quality after GEDAI, you shouldn't see the need to run ASR or ICA, it corrects everything very rigorously. You'd add unnecessary compute time.
>>
>> One of the advantage I forgot to mention actually is that GEDAI is alos pretty fast in parallel computing mode (in my experience) compared to doing clean_channels() + ASR + ICA + ICLabel as in my usual pipeline. Maybe 2 times faster, using the fast PICARD algorithm (on large recordings).
>>
>> A few things I wonder if someone has some answers (including the authors that I think are on this list)
>> - should we only use GEDAI on data referenced to average (CAR), or would the assumptions also work for other schemes like mastoids, REST, CSD, etc.
>> - can filtering be changed without disrupting the algorithms (e.g. highpass at 0.1, 05, 2 Hz, etc.)?
>> - should we remove bad channels prior for better performance (what I did) or not necessary?
>> - should bad channels be interpolated AFTER GEDAI to avoid potential rank difficient issues like with ASR and ICA, or not a problem here?
>> - has it been tested on other montages with more or less electroes (e.g. <32, 32, 128, etc.)?
>>
>> Thanks in advance for any insights.
>>
>> Cedric Cannard
>>
>> Sent with Proton Mail secure email.
>>
>> On Wednesday, April 1st, 2026 at 12:10 PM, Rob Coben via eeglablist <eeglablist at sccn.ucsd.edu> wrote:
>>
>>> Very interesting. I watched the video and have reviewed to primary article. It certainly looks promising and the logic behind it is sound.
>>>
>>> If anyone has tried using this I would love to hear your thoughts and experiences.
>>>
>>> Also, any opinion on using multiple tools together. For example, using GEDAI and followed up with iclabel or mara. Is it possible they are additive or is there redundancy to make that unnecessary?
>>>
>>> Thanks
>>>
>>> Rob
>>>
>>> > On Mar 31, 2026, at 11:17 AM, Makoto Miyakoshi via eeglablist <eeglablist at sccn.ucsd.edu> wrote:
>>> >
>>> > Hi Cedric,
>>> >
>>> > Thank you for your very quick response and sharing your impression.
>>> >
>>> > I think one nice additional advantage of GEDAI over ASR is that it is
>>> > probably more developer friendly. GEDAI is composed of more general
>>> > concepts: lead field and GED, while ASR is based more on free and unique
>>> > ideas supported by empirical parameter optimization. For example, the use
>>> > of truncated Gaussian, geometric median across covariance matrices, or its
>>> > Riemanian variant, etc etc., ending up with SD==20 being a de facto
>>> > default, which is obviously unusual but few can explain where exactly it
>>> > comes from.
>>> >
>>> > Hi Norbert,
>>> >
>>> > Yes, GEDAI supports EEGLAB. You can use it as an EEGLAB plugin. However,
>>> > you have to manually download the file and move them under /eeglab/plugins
>>> > as we used to do before the modern GUI-based management system was
>>> > introduced. Please see this page. You can find both the download package
>>> > and an instruction of how to 'install' it to EEGLAB
>>> >
>>> > https://urldefense.com/v3/__https://github.com/neurotuning/GEDAI-master__;!!Mih3wA!Ewu6iAWopqNjHfgqD4a5dp_o_Vr__gyjZ5WkzwJdtVEVKBZQjhjnGnfHoDT7gomOKfuHRXEwSD9QN5hM_p2yJKR8DKM$
>>> >
>>> > Makoto
>>> >
>>> > On Mon, Mar 30, 2026 at 6:58 PM Cedric Cannard via eeglablist <
>>> > eeglablist at sccn.ucsd.edu> wrote:
>>> >
>>> >> Hi Makoto,
>>> >>
>>> >> I’ve been playing with it and was impressed. It feels like the thresholds
>>> >> may be a little too aggressive to me (the ‘auto-‘ was better), but I
>>> >> haven’t done any formal comparisons yet. I’ll share here if I do. I’ve been
>>> >> also very curious to learn more but haven’t had the time to dig more yet.
>>> >>
>>> >>
>>> >> Cedric
>>> >>
>>> >> Sent from Proton Mail for iOS.
>>> >>
>>> >> -------- Original Message --------
>>> >> On Monday, 03/30/26 at 14:56 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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