Automated rejection and repair of epochs in M/EEG.
Project description
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 >= 3.8. 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)
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for autoreject-0.4.3-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a094bebeaea40572f479b2b489c14c54a0be4d57ae7f51ea269c754d2433f9c1 |
|
MD5 | fc4032e098c1f9150a1fb6c5e1321454 |
|
BLAKE2b-256 | 45cff34035d2de261064090cb46491b1b999f0b9711bc57da189ffe1b50df146 |