This repository is a conversion of the popular ICLabel classifier for Python. In addition, we provide improvements in the form of other models.
Scalp EEG is inherently noisy comprised commonly with heartbeat, eyeblink, muscle and movement artifacts. Independent component analysis (ICA) is a common method to remove artifacts, but rely on a human manually annotating with independent components (IC) are noisy and which are brain signal.
This package aims at automating that process conforming to the popular MNE-Python API for EEG, MEG and iEEG data.
MNE-ICALabel will estimate the labels of the ICA components given a MNE-Python Raw or Epochs object and an ICA instance using the ICA decomposition available in MNE-Python.
from mne_icalabel import label_components
# assuming you have a Raw and ICA instance previously fitted
label_components(raw, ica, method='iclabel')
The only current available method is 'iclabel'.
Stable version documentation. Dev version documentation.
To get the latest code using git, open a terminal and type:
git clone git://github.com/mne-tools/mne-icalabel.git
cd mne-icalabel
pip install -e .
or one can install directly using pip
pip install https://api.github.com/repos/mne-tools/mne-icalabel/zipball/main
Alternatively, you can also download a zip file of the latest development version.
If you are interested in contributing, please read the contributing guidelines.
Please visit the MNE forum to ask relevant questions.