A latent representation of brain networks based on EEG

Electroencephalography (EEG) is one of the most popular techniques to investigate normal as well as pathological cerebral mechanisms, as it allows to measure, non-invasively and in real-time, the brain activity. However, modeling EEG is still extremely challenging, because of its high-dimensionality, low signal-to-noise ratio, and high individual variability. This paper proposes a novel latent representation to study brain networks using EEG by means of a robust dynamic factor analysis (RDFA) approach. We investigate the ability of this latent representation to discriminate between two groups of subjects, i.e. alcoholic and healthy. By RDFA, we can extract a limited number of highly explanatory factors, as low as 8, significantly discriminating between the two groups. Also, we show that different brain patterns can be identified across different stimulation scenarios and EEG locations. Although preliminary, this work could give support to domain experts while providing some clinically-meaningful insights to identify common patterns as well as individual characteristics in different groups of healthy and pathological subjects.