🧠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 package is most useful for readers who want practical reference implementations of denoising and component-analysis methods, together with worked examples that show how to interpret the outputs.
The source code of the project is hosted on Github at the following address: nbara/python-meegkit
Quick start#
Install the package with pip:
pip install meegkit
Some ASR-related functionality requires optional dependencies. To install those as well, use:
pip install 'meegkit[extra]'
For development, documentation building, or testing, see the fuller installation guidance in the README.
Available modules#
Here is a list of the methods and techniques available in meegkit:
Artifact Subspace Reconstruction. |
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Canonical Correlation Analysis. |
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Denoising source separation. |
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Robust detrending. |
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Local Outlier Factor (LOF). |
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Real-time phase and amplitude estimation using resonant oscillators. |
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Rhythmic Entrainment Source Separation. |
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Sensor noise suppression. |
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Sparse time-artefact removal. |
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Task-Related Component Analysis. |
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Time-shift PCA. |
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Utility functions. |
Examples gallery#
A number of example scripts and notebooks are available.
If you are new to the package, a good starting sequence is:
example_asrfor a full artifact-removal workflow.example_dssfor a simple synthetic component-recovery example.example_trcaorexample_ressfor task-oriented spatial filtering.
Many examples are synthetic sanity checks with known ground truth, which makes them useful for understanding what each method is expected to recover.