[Eeglablist] Inconsistent results using clean_artifacts

Tue Mar 8 11:49:50 PST 2022

Thanks Velu for a good input.

> For a better estimation using RANSAC, you might use a higher value for
the variable num_samples (which is 50, by default).

Now we are curious what parameter for 'num_samples' makes near-perfect
stabilization.
Cristina, if you are going to test various numbers for testing, please
share the results with us!

Makoto

On Tue, Mar 8, 2022 at 11:41 AM Velu Prabhakar Kumaravel via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Dear Cristina,
>
> Glad that you resolved it.
>
> In this paper
> <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.frontiersin.org_articles_10.3389_fninf.2015.00016_full&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=6anBLYbp7QdHJFldL-5RHDUW8DOQDjVahfBeCIwkWRS7uwprMh8nahAmV7-npm_k&s=8bjRx5pTnD7zN6J5arUoUdyoWrU2e6a9vavVoWblXOc&e=
> >(PREP
> pipeline), you might find the reasons why RANSAC was proposed. For a better
> estimation using RANSAC, you might use a higher value for the variable
> num_samples (which is 50, by default).
>
> If you have larger datasets, these differences shouldn't be a problem. My
> suggestion would be to follow Makoto's alternate solution (to disable
> clean_rawdata for channel removal and to use the simplest measures).
>
> Best regards,
>
> Velu Prabhakar Kumaravel, Ph.D. Student
> Center for Mind/Brain Sciences,
> University of Trento, Italy
>
>
> On Tue, 8 Mar 2022 at 17:58, Gil Avila, Cristina via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
> > Dear Prabhakar and Makoto,
> >
> > Thank you for your responses. I have tried both
> > 1) Uncomment the lines in clean_channels() that fix the seed for random
> > stream and
> > 2) Keep the clean_channels() code as it as and fix in my code the seed of
> > the random number generator: rng('default');
> >
> > In both cases I get a stable set of bad channels although the set of bad
> > channels in 1) is different than the set of bad channels in 2). These
> > 'tricks' make stable the bad channel detection. Nevertheless, I am
> > concerned about the RANSAC algorithm itself. I believe it is suboptimal
> > that, depending on how the channels were initially chosen,  different
> sets
> > of bad channels are given as an output. I don't know if this will have an
> > impact on later results such as power effects between groups. Do you
> think
> > that this channel discrepancy will impact later results or do you think
> > that it will be diluted when looking into large datasets?
> >
> > Best,
> > Cristina
> >
> > -----Original Message-----
> > From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> > Sent: Thursday, 3 March 2022 18:40
> > To: eeglablist at sccn.ucsd.edu
> > Subject: Re: [Eeglablist] Inconsistent results using clean_artifacts
> >
> > Dear Cristina,
> >
> > Sorry for the inconvenience.
> >
> > > I am using the last version of clean_rawdata plugin and every time
> > > that I
> > run clean_rawdata on the same dataset I get a different set of 'bad
> > channels'. Is this problem fixed?
> >
> > According to this page, clean_channels() was updated 2 years ago. Without
> > testing, I guess it is probably not fixed.
> >
> >
> https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_sccn_clean-5Frawdata&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=8vkQdVejMdf_IHSTIf0e5McIIPD3TbqIxIXVpdRjCZxECTENaMdkB4EoFHSDinQX&s=yz8W2rwlcw_xyQhxNND6QqqHF0ahjP6l7vk_BsP5sgw&e=
> >
> > If I remember correctly, it uses RANSAC which uses 'repeated random
> > subsampling'. The unstable output is a nature of the algorithm and it
> > cannot be 'fixed'.
> > I know this is an annoying issue. We always prefer to use deterministic
> > approaches for the purpose of reproducibility.
> >
> > Here are my suggested work arounds.
> > 1. This is a little bit technical, but you can fix the seed of a random
> > number generator by using rng(). Theoretically, this guarantees the
> > complete reproducibility from the algorithm. In clean_channels() line
> 171,
> > you can see the following line commented out. I don't know why it is
> > commented out, but if you can fix the seed for the random number
> generator
> > there, it may work out. I would try rng() instead of what is there now.
> > %stream = RandStream('mt19937ar','Seed',435656);
> >
> > 2. Alternatively, you disable the electrode removal function in
> > clean_rawdata() and perform your own electrode rejection process.
> > Sometimes even a simple max value approach like the one used in
> > trimOutlier() is sufficient, so you may try it out.
> >
> > Makoto
> >
> >
> > On Wed, Mar 2, 2022 at 9:01 AM Gil Avila, Cristina via eeglablist <
> > eeglablist at sccn.ucsd.edu> wrote:
> >
> > > I would like to reopen a topic from 2020 regarding the consistency of
> > > clean_artifacts. I am using the last version of clean_rawdata plugin
> > > and every time that I run clean_rawdata on the same dataset I get a
> > > different set of 'bad channels'. Is this problem fixed? Specifically I
> > am running:
> > >
> > > EEG =
> > > pop_clean_rawdata(EEG,'FlatlineCriterion',5,'ChannelCriterion',0.8,'Li
> > > neNoiseCriterion',4,'Highpass',[0.25
> > > 0.75],'BurstCriterion','off','WindowCriterion','off','BurstRejection',
> > > 'off','Distance','Euclidian','WindowCriterionTolerances','off');
> > >
> > > Best,
> > > Cristina Gil
> > > --
> > > Cristina Gil Ávila - PhD candidate
> > > Department of Neurology
> > > Technische Universität München
> > > Munich, Germany
> > > cristina.gil at tum.de<mailto:cristina.gil at tum.de>
> > > painlabmunich.de<
> > > https://urldefense.proofpoint.com/v2/url?u=https-3A__www.painlabmunich
> > > .de_&d=DwIFAw&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm
> > > 1p6x6e36h3EglE&m=c_kRevMMCP43N5DUC96kTPQlI81ezyGf6D0jCBCbtNsjpI0SNRrFx
> > > tNyOi0J1P05&s=nZ9fhNHv4Zt2tPe1W18k_LDsgbW7YJcgl9PBRpNInAk&e=
> > > >
> > >
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