  Electroencephalography produces high-dimensional, stochastic data from which
it might be challenging to extract high-level knowledge about the phenomena of
interest. We address this challenge by applying the framework of variational
auto-encoders to 1) classify multiple pathologies and 2) recover the
neurological mechanisms of those pathologies in a data-driven manner. Our
framework learns generative factors of data related to pathologies. We provide
an algorithm to decode those factors further and discover how different
pathologies affect observed data. We illustrate the applicability of the
proposed approach to identifying schizophrenia, either followed or not by
auditory verbal hallucinations. We further demonstrate the ability of the
framework to learn disease-related mechanisms consistent with current domain
knowledge. We also compare the proposed framework with several benchmark
approaches and indicate its classification performance and interpretability
advantages.
