🧠 meegkit: EEG and MEG denoising in Python#

Introduction#

meegkit is a collection of EEG and MEG denoising techniques for Python 3.8+. Please feel free to contribute, or suggest new analyses. Keep in mind that this is mostly development code, and as such is likely to change without any notice. Also, while most of the methods have been fairly robustly tested, bugs can (and should!) be expected.

The source code of the project is hosted on Github at the following address: nbara/python-meegkit

To get started, follow the installation instructions in the README.

Available modules#

Here is a list of the methods and techniques available in meegkit:

asr

Artifact Subspace Reconstruction.

cca

Canonical Correlation Analysis.

dss

Denoising source separation.

detrend

Robust detrending.

lof

Local Outlier Factor (LOF).

phase

Real-time phase and amplitude estimation using resonant oscillators.

ress

Rhythmic Entrainment Source Separation.

sns

Sensor noise suppression.

star

Sparse time-artefact removal.

trca

Task-Related Component Analysis.

tspca

Time-shift PCA.

utils

Utility functions.

Indices and tables#