[Eeglablist] Code with tutorial to find optimal cut-off parameter of ASR

[Makoto Miyakoshi]

Dear Velu,

Relatedly, Hyeonseok and I have been working on a mod for the calibration
stage of ASR to process our Juggling data collected by Hiroyuki.
We will present the idea at the Mobi meeting 2022.
https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_ucsd.edu_mobi2022_&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=EPeRz5TzFSYYznxZnS2m3PtKD2iJKn761bzPggIVLh6SzXSz3DEp2Uxr9dBTzC9U&s=4KfHFFciD6WCO1XVfkQroJsihX_LWffBL6Eh2KXRqcI&e= 

The idea is to use single-frame order statistics across electrodes rather
than the default sliding window for selecting the calibration data. This
way, we can obtain more 'clean' data points without letting high-amplitude
artifacts into the calibration data (there is a default tolerance
value--that is, the default setting allows a small amount of outliers sneak
into the calibration data, up to 7.5% of electrodes; The proposed
method uses 0%.) The proposed method makes subsequent PC distributions more
Gaussian, which fits the assumption of ASR. Also, the proposed method seems
to be able to explain, at least partially, the reason why the
conventional empirically recommended values for the cutoff SD are unusually
high, such as SD == 20. We will show both simulation and empirical results.
Check out the MoBI 2022 conference!

Makoto

On Sat, Mar 12, 2022 at 11:46 AM Velu Prabhakar Kumaravel via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Hello EEGLABers,
>
> You might all know of the Artifacts Subspace Reconstruction (ASR)
> algorithm, an efficient algorithm especially to deal with non-stereotypical
> artifacts (prevalent in mobile EEG and/or developmental EEG). ASR has a
> user-defined parameter (ASR cut-off, k), which is set to 20, by default.
> This value should work in most cases, however, it would be valuable to find
> the optimal value for your datasets.
>
> In our Newborns EEG Artifacts Removal (NEAR
> <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.sciencedirect.com_science_article_pii_S1878929322000123&d=DwIBaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=j1r5OrQy6HMNTNYtzCAuvh_-7ZoAcHj0JoPEHnmvWsG-tfFHZVxF5a1OUYVuDzGk&s=UqF8-uu_HoSLuhsrW2mpvLy3vaNc0wABmPRsjtrH2ag&e=
> >)
> pipeline, we proposed a systematic grid search approach to find the optimal
> k. I wrote a short tutorial which can be found in this link
> <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_vpKumaravel_vpKumaravel.github.io_wiki_How-2Dto-2Dfind-2Dthe-2Doptimal-2Dhyperparameter-2Dfor-2DArtifacts-2DSubspace-2DReconstruction-2D-28ASR-29-2Dalgorithm-2Dto-2Dclean-2DEEG-2Dartifacts-253F&d=DwIBaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=j1r5OrQy6HMNTNYtzCAuvh_-7ZoAcHj0JoPEHnmvWsG-tfFHZVxF5a1OUYVuDzGk&s=5HGabx27vchRkelZqWRzWDTatD1X1sWTMWfFwDZm-F0&e=
> >along
> with the EEGLAB compatible MATLAB code.
>
> Hope this is useful and let me know your feedback in case you tried it out.
>
> Best regards,
>
> Velu Prabhakar Kumaravel, Ph.D. Student
> Center for Mind/Brain Sciences,
> University of Trento, Italy
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