An attention-based architecture for EEG classification

Emerging studies in the deep learning community focus on techniques aimed to identify which part of a graph can be suitable for making better decisions and best contributes to an accurate inference. These researches (i.e., “attentional mechanisms” for graphs) can be applied effectively in all those situations in which it is not trivial to capture dependency between the involved entities while discharging useless information. This is the case, e.g., of functional connectivity in human brain, where rapid physiological changes, artifacts and high inter-subject variability usually require highly trained clinical expertise. In order to evaluate the effectiveness of the attentional mechanism in such critical situation, we consider the task of normal vs abnormal EEG classification using brain network representation of the corresponding EEG recorded signals.