  In this study, we leverage a deep learning-based method for the automatic
diagnosis of schizophrenia using EEG brain recordings. This approach utilizes
generative data augmentation, a powerful technique that enhances the accuracy
of the diagnosis. To enable the utilization of time-frequency features,
spectrograms were extracted from the raw signals. After exploring several
neural network architectural setups, a proper convolutional neural network
(CNN) was used for the initial diagnosis. Subsequently, using Wasserstein GAN
with Gradient Penalty (WGAN-GP) and Variational Autoencoder (VAE), two
different synthetic datasets were generated in order to augment the initial
dataset and address the over-fitting issue. The augmented dataset using VAE
achieved a 3.0\% improvement in accuracy reaching up to 99.0\% and yielded a
lower loss value as well as a faster convergence. Finally, we addressed the
lack of trust in black-box models using the Local Interpretable Model-agnostic
Explanations (LIME) algorithm to determine the most important superpixels
(frequencies) in the diagnosis process.
