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

[Rab Nawaz]

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