A review of Graph Neural Networks for Electroencephalography data analysis

Electroencephalography (EEG) sensors are flexible and non-invasive sensoring devices for the measurement of electrical brain activity which is extensively used in some areas of clinical practice and psychological/psychiatric research, such as epilepsy, sleep, emotion, and brain computer interfaces. Although EEG sensor do not provide actual brain localizations of the activity sources, they allow to study brain functional connectivity. In this paper we review current application of a specific family of computational methods, the Graph Neural Networks (GNN) to the analysis of EEG data. GNNs appear to be well suited to EEG data modeling as they deal with signals whose domain is defined by a graph instead of a regular lattice in Euclidean space. Readings of EEG electrodes fall in this category, hence the increasing research activity on the application of GNNs to EEG data.