mattbit/wavelet-wqn-acha

WQN algorithm supporting code (ACHA)

Supporting code for "The WQN algorithm to adaptively correct artifacts in the EEG signal" by Matteo Dora, Stéphane Jaffard, David Holcman.

Setup

The code was written for Python 3.10 (but can probably run on previous versions without modification). The dependencies can be installed via poetry. After installing poetry, the project can be set up with:

poetry install

Otherwise, one can manually install the dependencies listed in pyproject.toml file with their preferred tool, e.g:

pip install scipy numpy matplotlib PyWavelets ipykernel fbm h5py pandas tqdm

Figures and results

The folder acha_scripts contains Python scripts to generate all figures and results. They can be run like this:

PYTHONPATH=. poetry run python acha_scripts/[name_of_the_script].py 

The generated output files will be placed in the ./output folder.

Help

If you need help running the code don't hesitate to contact the author at matteo.dora@ieee.org.