This is a library to automatically reject bad trials and repair bad sensors in magneto-/electroencephalography (M/EEG) data.
We recommend the Anaconda Python distribution
and a Python version >= 3.7.
To obtain the stable release of autoreject, you can use pip:
pip install -U autoreject
Or conda:
conda install -c conda-forge autoreject
If you want the latest (development) version of autoreject, use:
pip install https://api.github.com/repos/autoreject/autoreject/zipball/master
If you do not have admin privileges on the computer, use the --user flag
with pip.
To check if everything worked fine, you can do:
python -c 'import autoreject'
and it should not give any error messages.
Below, we list the dependencies for autoreject.
All required dependencies are installed automatically when you install autoreject.
mne (>=0.24)
numpy (>=1.20)
scipy (>=1.6)
scikit-learn (>=0.24)
joblib
matplotlib (>=3.3)
Optional dependencies are:
tqdm (for nice progress-bars when setting verbose=True)
h5io (for writing autoreject objects using the HDF5 format)
openneuro-py (>= 2021.7, for fetching data from OpenNeuro.org)
The easiest way to get started is to copy the following three lines of code in your script:
>>> from autoreject import AutoReject
>>> ar = AutoReject()
>>> epochs_clean = ar.fit_transform(epochs)
This will automatically clean an epochs object read in using MNE-Python. To get the rejection dictionary, simply do:
>>> from autoreject import get_rejection_threshold
>>> reject = get_rejection_threshold(epochs)
We also implement RANSAC from the PREP pipeline. The API is the same:
>>> from autoreject import Ransac
>>> rsc = Ransac()
>>> epochs_clean = rsc.fit_transform(epochs)
For more details check out the example to automatically detect and repair bad epochs.
Please use the GitHub issue tracker to report bugs.
[1] Mainak Jas, Denis Engemann, Federico Raimondo, Yousra Bekhti, and Alexandre Gramfort, “Automated rejection and repair of bad trials in MEG/EEG.” In 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016.
[2] Mainak Jas, Denis Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre Gramfort. 2017. “Autoreject: Automated artifact rejection for MEG and EEG data”. NeuroImage, 159, 417-429.