khuongav/Graphical-Adversarial-Modeling-of-EEG

Codebase for Composing graphical models with generative adversarial networks for EEG signal modeling - ICASSP 22

Codebase for "Composing graphical models with generative adversarial networks for EEG signal modeling"

Paper link: https://ieeexplore.ieee.org/document/9747783

Directed graphical models for EEG signal modeling

Generative process:

Inference process:

Each time step corresponds to a $\delta$-second multi-channel signal. Shaded nodes represent observed variables. Clear nodes represent latent variables. Directed edges indicate statistical dependencies between variables.

Variables $k$, $z$, and $c$ represent Gaussian mixture indicators, sampling noise, and conditions, respectively. $v_t$ are latent states and $x_t$ are EEG segments.

Setup

To install the dependencies, you can run in your terminal:

pip install -r requirements.txt

Experimental dataset can be downloaded at: https://zenodo.org/record/8408937

Usage

The code is structured as follows:

• data.py contains functions to transform and feed the data to the model.
• models.py defines deep neural network architectures.
• utils.py has utilities for evaluation and plottings.
• train.py is the main entry to run the training process.
• eval.ipynb runs the evaluation.

Citation

If you find this code helpful, please cite our paper:

@inproceedings{vo2022composing,
    title={Composing Graphical Models with Generative Adversarial Networks for EEG Signal Modeling},
    author={Vo, Khuong and Vishwanath, Manoj and Srinivasan, Ramesh and Dutt, Nikil and Cao, Hung},
    booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
    pages={1231--1235},
    year={2022},
    organization={IEEE}
}