EEG-to-EEG: Scalp-to-Intracranial EEG Translation using a Combination of Variational Autoencoder and Generative Adversarial Networks

A generative adversarial network (GAN) enables mapping a data sample from one domain to another one. It has extensively been employed in image-to-image and text-toimage translation. We propose an EEG-to-EEG translation model based on a GAN structure in which a conditional GAN (cGAN) is combined with a variational autoencoder (VAE), named as VAE-cGAN. Scalp EEG (sEEG) is plagued by noise and suffers from low resolution. On the other hand, intracranial EEG (iEEG) recordings enjoy high resolution. Here, we consider the task of mapping the sEEG to iEEG to enhance the sEEG resolution.