MEEGLET

Morlet wavelets for M/EEG analysis, [ˈmiːglɪt]

This package provides a lean implementation of Morlet wavelets (Morlet et al. 1982) designed for power-spectral analysis of M/EEG resting-state signals (Hipp et al. 2012; Bomatter et al. 2023).

Code 
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 {bomatter2023,
    author = {Philipp Bomatter and Joseph Paillard and Pilar Garces and Joerg F Hipp and Denis A Engemann},
    title = {Machine learning of brain-specific biomarkers from EEG},
    elocation-id = {2023.12.15.571864},
    year = {2023},
    doi = {10.1101/2023.12.15.571864},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2023/12/21/2023.12.15.571864},
    eprint = {https://www.biorxiv.org/content/early/2023/12/21/2023.12.15.571864.full.pdf},
    journal = {bioRxiv}
}

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}
}

References

Bomatter, Philipp, Joseph Paillard, Pilar Garces, Joerg F Hipp, and Denis A Engemann. 2023. “Machine Learning of Brain-Specific Biomarkers from EEG.” bioRxiv.
Hipp, Joerg F, David J Hawellek, Maurizio Corbetta, Markus Siegel, and Andreas K Engel. 2012. “Large-Scale Cortical Correlation Structure of Spontaneous Oscillatory Activity.” Nature Neuroscience 15 (6): 884–90.
Morlet, J., G. Arens, E. Fourgeau, and D. Giard. 1982. “Wave Propagation and Sampling Theory—Part II: Sampling Theory and Complex Waves.” GEOPHYSICS 47 (2): 222–36. https://doi.org/10.1190/1.1441329.