Automatically identify independent components associated with artifact

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Hi,

We do hyperscanning research. Our colleague developed this lovely python
module for analyzing hyperscanning data. You may want to try this
Hyperscanning module, HyPyP <https://github.com/GHFC/HyPyP&gt;\. Or you can see
the tutorial of using the module particularly on the *ICA correction
section*
<https://github.com/GHFC/HyPyP/blob/master/tutorial/getting_started.ipynb&gt;
and *AutoReject* for ICA.

You can use the function there for the automatic rejection of ICA, even
though your research may not be related to hyperscanning. But you may want
to use it. Hope that helps.

Cheers,
IG

Today's Topics:

   1. Automatically identify independent components associated with
      artifact (balandongiv at gmail.com)

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Message: 1
Date: Tue, 18 Aug 2020 23:30:00 +0800
From: <balandongiv at gmail.com>
Subject: [Mne_analysis] Automatically identify independent components
        associated with artifact
To: <mne_analysis at nmr.mgh.harvard.edu>
Message-ID: <008a01d67574$70aa1860$51fe4920$@gmail.com>
Content-Type: text/plain; charset="us-ascii"

        External Email - Use Caution

Is there any available module/package to automatically identify
independent
components associated with artifacts in MNE or any module that can be
incorporated along with the mne pipeline? For example, in EEGlab, they have
the plugin ADJUST that can automate the artifact identification and removal
in EEG data.

Regards

Rodney

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