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Complex-valued Morlet wavelets for EEG signal analysis and feature engineering.

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MEEGLET

Morlet wavelets for M/EEG analysis, [ˈmiːglɪt]

This package provides a lean implementation of Morlet wavelets designed for power-spectral analysis of M/EEG resting-state signals.

  • Distinct frequency-domain parametrization of Morlet wavelets
  • Established spectral M/EEG metrics share same wavelet convolutions
  • Harmonized & tested Python and MATLAB implementation numerically equivalent
  • Comprehensive mathematical documentation
import matplotlib.pyplot as plt
from meeglet import define_frequencies, define_wavelets, plot_wavelet_family

foi, sigma_time, sigma_freq, bw_oct, qt = define_frequencies(
    foi_start=1, foi_end=32, bw_oct=1, delta_oct=1
)

wavelets = define_wavelets(
    foi=foi, sigma_time=sigma_time, sfreq=1000., density='oct'
)

plot_wavelet_family(wavelets, foi, fmax=64)
plt.gcf().set_size_inches(9, 3)

Documentation

Background overview on scope, rationale & design choices
Python tutorials M/EEG data analysis examples
Python API Documentation of Python functions and unit tests
MATLAB functionality MATLAB documentation and data analysis example

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Installation

from PyPi

In your environment of choice, use pip to install meeglet:

pip install meeglet

from the sources

Please clone the software, consider installing the dependencies listed in the `environment.yml.

Then do in your conda/mamba environment of choice:

pip install -e .

Citation

When using our package, please cite our two reference articles:

Python implementation and covariance computation.

@article{bomatter2024,
	author = {Bomatter, Philipp and Paillard, Joseph and Garces, Pilar and Hipp, J{\"o}rg and Engemann, Denis-Alexander},
	title = {Machine learning of brain-specific biomarkers from EEG},
	year = {2024},
	journal = {eBioMedicine},
	url = {https://doi.org/10.1016/j.ebiom.2024.105259},
	date = {2024/08/05},
	publisher = {Elsevier},
	isbn = {2352-3964},
	month = {2024/08/06},
	volume = {106},
}

General methodology, MATLAB implementation and power-envelope correlations.

@article{hipp2012large,
  title={Large-scale cortical correlation structure of spontaneous oscillatory activity},
  author={Hipp, Joerg F and Hawellek, David J and Corbetta, Maurizio and Siegel, Markus and Engel, Andreas K},
  journal={Nature neuroscience},
  volume={15},
  number={6},
  pages={884--890},
  year={2012},
  publisher={Nature Publishing Group US New York}
}

Related software

M/EEG features based on Morlet wavelets using the more familiar time-domain parametrization can be readily computed is sevaral major software packages for M/EEG analysis: