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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