meeglet 0.0.1rc6

Morlet Wavelets for M/EEG analysis

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

Use the left sidebar for navigating conveniently!

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}
}

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:

• FieldTrip
• BrainStorm
• MNE
• MNE-Connectivity