[Eeglablist] Inconsistent results clean_artifacts follow up

[Daniele Scanzi]

Dear Arno,

I will submit an issue on Github. I just did some more testing and I am
more confused. If I run clean_artifacts on the same dataset in a loop, the
results are consistent. However, if I load the dataset and run the pipeline
for every iteration, I get insatiable results. I'll provide the dataset and
code in the Issue.

Thank you,

Daniele

On Thu, 10 Mar 2022 at 08:36, Delorme, Arnaud via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Dear Darniele,
>
> Would you mind submitting a GitHub issue so we can assess if there is
> still an issue with reproducibility?
>
>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_sccn_clean-5Frawdata_issues&d=DwIFAg&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=mYXzpgbn7EKV7D3HEpzC-CiQG2eyPSRII0WM5ZtujakztlCatt8gtKGKASU6Mfsw&s=R-OtAGJc1hv0jQ0IctfNZlPs1ZHpUXX9-UTxbijR0TY&e=
>
> Arno
>
> > On Mar 4, 2022, at 9:47 PM, 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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