Generating realistic neurophysiological time series with denoising diffusion probabilistic models

This repository contains research code for the preprint Generating realistic neurophysiological time series with denoising diffusion probabilistic models.

It contains all the scripts to run the experiments presented in the paper.
To run the experiments, first download the datasets (links in the paper) and change the filepath parameter in the dataset configuration files in conf/dataset. To save all outputs locally, set the save_path parameter in conf/base to the desired location. Experiment tracking and saving with Weights & Biases is also supported, but disabled by default.
Finally, use the shell scripts provided in scripts to run the experiments!

Additionally, an example jupyter notebook example_ner_notebook.ipynb, where a diffusion model is trained to generate the BCI Challenge @ NER 2015 data, is provided together with the data.

Coming soon: Notebooks for plotting the figures!

Usage

Install dependencies:

git clone https://github.com/mackelab/neural_timeseries_diffusion.git
cd neural_timeseries_diffusion
pip install -e .

Run shell scripts:

cd scripts
./<name_of_script>.sh

Run the example jupyter notebook:

• Make sure that the jupyter package is installed
• Open and execute the notebook