MNE-ICALabel: Automatically annotating ICA components with ICLabel in Python
Adam Li, Jacob Feitelberg, Anand Prakash Saini, Richard Höchenberger, Mathieu Scheltienne · The Journal of Open Source Software · 2022
Scalp electroencephalography (EEG) and magnetoencephalography (MEG) analysis is typically very noisy and contains various non-neural signals, such as heartbeat artifacts.Independent component analysis (ICA) is a common procedure to remove these artifacts (Bell & Sejnowski, 1995).However, removing artifacts requires manual annotation of ICA components, which is subject to human error and very laborious when operating on large datasets.This work makes the popular ICLabel model (Pion-Tonachini et al., 2019) available in Python by creating a software package compatible with the MNE-Python [from v1.1; Gramfort et al. (2013)] software toolkit in a modern PyTorch format (Paszke et al., 2019).The ICLabel model was previously only available in an outdated version of TensorFlow that was no longer supported, and migrating the model now to an updated PyTorch version will ensure the model will not break due to unmaintained versions of software.This enables the automatic labeling of ICA components, improving the preprocessing and analysis pipeline of electrophysiological data.