# 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__
```python
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:
```bash
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:
``` bash
pip install -e .
```
## Citation
When using our package, please cite our two reference articles:
Python implementation and covariance computation.
``` bibtex
@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.
``` bibtex
@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](https://www.fieldtriptoolbox.org/)
- [BrainStorm](https://neuroimage.usc.edu/brainstorm/)
- [MNE](https://mne.tools/stable/index.html)
- [MNE-Connectivity](https://mne.tools/mne-connectivity/stable/index.html)
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"description": "# MEEGLET\n> Morlet wavelets for M/EEG analysis, [\u02c8mi\u02d0gl\u026at]\n\nThis package provides a lean implementation of Morlet wavelets *designed for power-spectral analysis of M/EEG resting-state signals*.\n\n- Distinct __frequency-domain parametrization of Morlet wavelets__\n- Established __spectral M/EEG metrics__ share same wavelet convolutions\n- Harmonized & tested __Python__ and __MATLAB__ implementation __numerically equivalent__\n- Comprehensive __mathematical documentation__\n\n\n```python\nimport matplotlib.pyplot as plt\nfrom meeglet import define_frequencies, define_wavelets, plot_wavelet_family\n\nfoi, sigma_time, sigma_freq, bw_oct, qt = define_frequencies(\n foi_start=1, foi_end=32, bw_oct=1, delta_oct=1\n)\n\nwavelets = define_wavelets(\n foi=foi, sigma_time=sigma_time, sfreq=1000., density='oct'\n)\n\nplot_wavelet_family(wavelets, foi, fmax=64)\nplt.gcf().set_size_inches(9, 3)\n```\n\n## Documentation\n| | |\n|:-------------------------|:-------------------------------------------------|\n| __Background__ | overview on scope, rationale & design choices |\n| __Python tutorials__ | M/EEG data analysis examples |\n| __Python API__ | Documentation of Python functions and unit tests |\n| __MATLAB functionality__ | MATLAB documentation and data analysis example |\n\nUse the left sidebar for navigating conveniently!\n\n## Installation\n\n## from PyPi\n\nIn your environment of choice, use pip to install meeglet:\n\n```bash\npip install meeglet\n```\n\n### from the sources\n\nPlease clone the software, consider installing the dependencies listed in the \\`environment.yml.\n\nThen do in your conda/mamba environment of choice:\n\n``` bash\npip install -e .\n```\n\n## Citation\n\nWhen using our package, please cite our two reference articles:\n\nPython implementation and covariance computation.\n\n``` bibtex\n@article{bomatter2024,\n\tauthor = {Bomatter, Philipp and Paillard, Joseph and Garces, Pilar and Hipp, J{\\\"o}rg and Engemann, Denis-Alexander},\n\ttitle = {Machine learning of brain-specific biomarkers from EEG},\n\tyear = {2024},\n\tjournal = {eBioMedicine},\n\turl = {https://doi.org/10.1016/j.ebiom.2024.105259},\n\tdate = {2024/08/05},\n\tpublisher = {Elsevier},\n\tisbn = {2352-3964},\n\tmonth = {2024/08/06},\n\tvolume = {106},\n}\n```\n\nGeneral methodology, MATLAB implementation and power-envelope correlations.\n\n``` bibtex\n@article{hipp2012large,\n title={Large-scale cortical correlation structure of spontaneous oscillatory activity},\n author={Hipp, Joerg F and Hawellek, David J and Corbetta, Maurizio and Siegel, Markus and Engel, Andreas K},\n journal={Nature neuroscience},\n volume={15},\n number={6},\n pages={884--890},\n year={2012},\n publisher={Nature Publishing Group US New York}\n}\n```\n\n## Related software\n\nM/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:\n\n- [FieldTrip](https://www.fieldtriptoolbox.org/)\n- [BrainStorm](https://neuroimage.usc.edu/brainstorm/)\n- [MNE](https://mne.tools/stable/index.html)\n- [MNE-Connectivity](https://mne.tools/mne-connectivity/stable/index.html)\n",
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