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[Daniel Jones]

Best Regards,
Daniel K. Jones, Ph.D., P.E.
Associate Professor
SUNY Polytechnic Institute
(315)792-7121, or -7234
1207 Donovan Hall
________________________________
From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of Daniele Scanzi via eeglablist <eeglablist at sccn.ucsd.edu>
Sent: Wednesday, March 9, 2022 1:38 PM
To: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
Cc: EEGLAB List <eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] Inconsistent results clean_artifacts follow up

Dear Makoto,

Yes, in general the inconsistencies appears when the recording is short and
when there is low-frequency power <1Hz. Although, the longer the recording,
the more low-frequency high-power can be present without creating many
problems.

I'll try to add a couple of test results and datasets on the Issue opened
on Github today.

Thank you for your work, really appreciate it!

Daniele

On Thu, 10 Mar 2022, 07:20 Makoto Miyakoshi via eeglablist, <
eeglablist at sccn.ucsd.edu> wrote:

> Dear Daniele,
>
> Thank you for your input on this issue. So the results from th channel
> rejection by clean_artifact() becomes more random when
> 1. data are short
> 2. data contains a lot of low freq power (<1Hz?)
> This is a great starting point.
> Actually, I've been working on the 'ASR modding project'. I'll suggest an
> additional optimization project on this point to the project members.
>
> Makoto
>
> On Sat, Mar 5, 2022 at 8:34 AM Daniele Scanzi via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
> > Hi there,
> >
> > I thought to follow up this thread here (
> > https://sccn.ucsd.edu/pipermail/eeglablist/2020/015456.html) about
> > inconsistent results using *clean_artifacts* to detect and remove bad
> > channels.
> >
> > I was processing a dataset and encountered the same problem. After
> > investigating for a while, I observed that the results are inconsistent
> > when:
> >
> >
> >    1. The recording is short (less than 10 minutes recorded at 1000Hz
> >    before downsampling at 250Hz). So, in general, when the data has less
> > than
> >    150.000 samples. The less the number of samples, the more inconsistent
> > the
> >    results are.
> >    2. The cut-off for the low-pass filter is 0.1 (as used in ERP
> research).
> >
> >
> > It seems that these two conditions need to be both met to run into
> > inconsistencies. Furthermore, the two conditions appear to be related. In
> > general, the lower the number of sample, the higher the cut-off at which
> > inconsistencies appears.
> >
> > As reported in the thread above, I suspect that the reason relies on the
> > use of *rand()* in *clean_channels *(line 187). Setting rng('default')
> > before calling *clean_artifacts* produces consistent results. Although
> they
> > are not reliable as it is unsure whether the flagged channels are
> actually
> > noisy or not.
> >
> > I do not think there is much that needs to be done (maybe having a
> message
> > in case someone is trying to use a short dataset?). I will try to open an
> > issue on Github to introduce this. But I thought that having this thread
> > here might help someone in the future looking on Google for this
> behaviour.
> >
> > Thank you for your work,
> >
> > Daniele Scanzi
> > _______________________________________________
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