🧠 `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.

Examples gallery#

A number of example scripts and notebooks are available:

Examples gallery

Indices and tables#