MNE-Features

|GitHub Actions| |Codecov|

.. |GitHub Actions| image:: https://github.com/mne-tools/mne-features/actions/workflows/main.yml/badge.svg .. _GitHub Actions: https://github.com/mne-tools/mne-features/actions/workflows/main.yml

.. |Codecov| image:: http://codecov.io/github/mne-tools/mne-features/coverage.svg?branch=master .. _Codecov: http://codecov.io/github/mne-tools/mne-features?branch=master

This repository provides code for feature extraction with M/EEG data. The documentation of the MNE-Features module is available at: documentation <https://mne-tools.github.io/mne-features/index.html>_.

Installation

To install the package, the simplest way is to use pip to get the latest release::

$ pip install mne-features

Or if you prefer conda::

$ conda install --channel=conda-forge mne-features

Or to get the latest version of the code::

$ pip install git+https://github.com/mne-tools/mne-features.git#egg=mne_features

Dependencies

These are the dependencies to use MNE-Features:

• numpy (>=1.17)
• matplotlib (>=1.5)
• scipy (>=1.0)
• numba (>=0.46.0)
• llvmlite (>=0.30)
• scikit-learn (>=0.21)
• mne (>=0.18.2)
• PyWavelets (>=0.5.2)
• pandas (>=0.25)

Cite

If you use this code in your project, please cite::

Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT,
"An ensemble learning approach to detect epileptic seizures from long intracranial EEG recordings"
Proc. IEEE ICASSP Conf. 2018