Repo "Modeling EEG data distribution with a Wasserstein Generative Adversarial Network (WGAN) to predict RSVP Events"

• Accepted in IEEE Transactions on Neural Systems & Rehabilitation Engineering

A Wasserstein Generative Adversarial with Gradient Penalty (WGAN-GP) is proposed to generate and classify electroencephalography(EEG) data of a Rapid Visual Presentation (RSVP) experiment.

The preprint is available @ https://arxiv.org/ftp/arxiv/papers/1911/1911.04379.pdf

Installation and Dependancies:

1. python 3.6.4
2. tensorflow 1.12
3. keras 2.2.4
4. matlab for data preprocessing
5. EEGLab - matlab package (optional) for data visualization