[Eeglablist] Automatic ICs rejection from the EEG data in MNE Python

[Rab Nawaz]

Dear Anna,

Thank you so much for sharing it. It is indeed a good option to start with.
I will definitely consider it.

Thanks


*Rab Nawaz,* B.Engg., Ph.D.
Postdoctoral research associate

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02/24/22,
01:15:55 PM

On Wed, Feb 23, 2022 at 4:05 PM Anna Bánki <anna.banki at univie.ac.at> wrote:

> Dear Rab,
>
>
> Maybe you already know about it, but cleaning the data before and after
> ICA could be done automatically by AutoReject in MNE Python:
>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1016_j.neuroimage.2017.06.030&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=xM_r2sYc2if0a_j1VooUDVMF053qlAQHAGaYJ0dCJtnVeuFnHoI_LY4msdWi3Hy9&s=LYmQo5icyFtWjFJl_QiA3GISV28wgtMR90No7oDbsm4&e= 
>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__autoreject.github.io_stable_index.html&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=xM_r2sYc2if0a_j1VooUDVMF053qlAQHAGaYJ0dCJtnVeuFnHoI_LY4msdWi3Hy9&s=MG34vEbKy4he8eT6kHH3yDqn0p96bb2gijyO0ZXtV2M&e= 
>
>
> Also see this thread:
> https://urldefense.proofpoint.com/v2/url?u=https-3A__mne.discourse.group_t_eeg-2Dprocessing-2Dpipeline-2Dwith-2Dautoreject_3443&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=xM_r2sYc2if0a_j1VooUDVMF053qlAQHAGaYJ0dCJtnVeuFnHoI_LY4msdWi3Hy9&s=Svel8wrD39qLtUO4TMSpY-hVYUM4gEqo3Ilc7kfzJsg&e= 
>
>
> Hope this helps! I am not aware of automated ICA in MNE Python but I have
> not used MNE a while ago.
>
>
> Best,
>
> Anna
>
>
>
> ------------------------------
> *From:* eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of Rab
> Nawaz via eeglablist <eeglablist at sccn.ucsd.edu>
> *Sent:* 23 February 2022 16:53:09
> *To:* eeglablist
> *Subject:* [Eeglablist] Automatic ICs rejection from the EEG data in MNE
> Python
>
> Hi EEGLABers,
>
>
>
> As in the EEGLAB we know there are multiple ICA classification methods
> available as a plugins (MARA (Winkler et al., 2011), ADJUST (Mognon et al.,
> 2011), SASICA, IC-MARC) for some near-automatic methods for removing
> unhealthy ICs to clean the data from artifacts.
>
>
>
> I am restricted to use an open-source tool therefore I choose Python. I am
> trying to perform data cleaning based on the ICA in MNE Python and looking
> for something automatic that helps in automatic ICs rejection. In MNE
> Python, there is a template matching method that uses the artifacted ICs
> from one person as ground and correlates it with the ICs of new EEG to pick
> the ICs which have strong correlation with the artifacted one. But, I am
> looking for something similar to the implementation of MARA or ADJUST in
> MNE Python. Do you know any materials that can help me in this regard? Or
> something else (other than template matching) that can help in selecting
> the artifacted ICs automatically in Python?
>
>
>
> Thanks
>
> Rab
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