Morlet Wavelets for M/EEG analysis
Project description
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 {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:
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