autoreject

This is a library to automatically reject bad trials and repair bad sensors in magneto-/electroencephalography (M/EEG) data.

The documentation can be found under the following links:

for the stable release
for the latest (development) version

Installation

We recommend the Anaconda Python distribution and a Python version >= 3.11. We furthermore recommend that you install autoreject into an isolated Python environment. 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://github.com/autoreject/autoreject/archive/refs/heads/main.zip

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 (>=1.5.0)
numpy (>=1.21.2)
scipy (>=1.7.1)
scikit-learn (>=1.0.0)
joblib
matplotlib (>=3.5.0)

Optional dependencies are:

openneuro-py (>= 2021.10.1, for fetching data from OpenNeuro.org)

Quickstart

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)  # doctest: +SKIP

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)  # doctest: +SKIP

We also implement RANSAC from the PREP pipeline (see PyPREP for a full implementation of the PREP pipeline). The API is the same:

>>> 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.

Bug reports

Please use the GitHub issue tracker 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”. 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.

Metadata

Release files for autoreject 0.5.1

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Source distribution (sdist)

Source distribution for autoreject 0.5.1
File	Size	Uploaded
autoreject-0.5.1.tar.gz	27.8 kB	Sep 23, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for autoreject 0.5.1
File	Interpreter	ABI	Platform	Reset
autoreject-0.5.1-py3-none-any.whl 30.1 kB Sep 23, 2026	Python 3	none	any	Details

Total release size: 57.9 kB

Release files / autoreject-0.5.1.tar.gz

Download URL	autoreject-0.5.1.tar.gz
Size	27.8 kB
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Uploaded via	`twine/7.0.0 CPython/3.14.7`

Release files / autoreject-0.5.1-py3-none-any.whl

Download URL	autoreject-0.5.1-py3-none-any.whl
Size	30.1 kB
Tags	Python 3
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Uploaded via	`twine/7.0.0 CPython/3.14.7`