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. However, removing artifacts requires manual annotation of ICA components, which is subject to human error and very laborious when operating on large datasets. The first versions of mne-icalabel replicated the popular ICLabel model for Python.
The project was developed in collaboration with Adam Li (github handle: @adam2392) and Jacob Feitelberg (github handle: @jacobf18).