Installation

We recommend the Anaconda Python distribution <https://www.anaconda.com/>_ and a Python version >= 3.8. 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 (>=1.0)
numpy (>=1.20.2)
scipy (>=1.6.3)
scikit-learn (>=0.24.2)
joblib
matplotlib (>=3.4.0)

Optional dependencies are:

openneuro-py (>= 2021.10.1, for fetching data from OpenNeuro.org <https://openneuro.org>_)

Quickstart

The easiest way to get started is to copy the following three lines of code in your script:

.. code:: python

>>> from autoreject import AutoReject
>>> ar = AutoReject()
>>> epochs_clean = ar.fit_transform(epochs)  # doctest: +SKIP

This will automatically clean an epochs object read in using MNE-Python. To get the rejection dictionary, simply do:

.. code:: python

>>> from autoreject import get_rejection_threshold
>>> reject = get_rejection_threshold(epochs)  # doctest: +SKIP

We also implement RANSAC from the PREP pipeline <https://doi.org/10.3389/fninf.2015.00016>_ (see PyPREP <https://github.com/sappelhoff/pyprep>_ for a full implementation of the PREP pipeline). The API is the same:

.. code:: python

>>> from autoreject import Ransac
>>> rsc = Ransac()
>>> epochs_clean = rsc.fit_transform(epochs)  # doctest: +SKIP

For more details check out the example to automatically detect and repair bad epochs <https://autoreject.github.io/stable/_images/sphx_glr_plot_auto_repair_001.png>_.

Bug reports

Please use the GitHub issue tracker <https://github.com/autoreject/autoreject/issues>_ to report bugs.

Cite

[1] Mainak Jas, Denis Engemann, Federico Raimondo, Yousra Bekhti, and Alexandre Gramfort, "Automated rejection and repair of bad trials in MEG/EEG <https://hal.archives-ouvertes.fr/hal-01313458/document>_." 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 <http://www.sciencedirect.com/science/article/pii/S1053811917305013>_". NeuroImage, 159, 417-429.

Keywords

electroencephalography eeg magnetoencephalography meg preprocessing analysis

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