Learning Generative Factors of EEG Data with Variational auto-encoders
This work addresses the problem of interpreting EEG data for medical diagnosis, specifically for pathologies like schizophrenia, but it is incremental as it applies an existing method to a new domain.
The paper tackled the challenge of extracting high-level knowledge from high-dimensional, stochastic EEG data by applying variational auto-encoders to classify multiple pathologies and recover their neurological mechanisms, demonstrating applicability to identifying schizophrenia and auditory verbal hallucinations with classification performance and interpretability advantages.
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.