  Convolutional neural networks (CNN) have been frequently used to extract
subject-invariant features from electroencephalogram (EEG) for classification
tasks. This approach holds the underlying assumption that electrodes are
equidistant analogous to pixels of an image and hence fails to explore/exploit
the complex functional neural connectivity between different electrode sites.
We overcome this limitation by tailoring the concepts of convolution and
pooling applied to 2D grid-like inputs for the functional network of electrode
sites. Furthermore, we develop various graph neural network (GNN) models that
project electrodes onto the nodes of a graph, where the node features are
represented as EEG channel samples collected over a trial, and nodes can be
connected by weighted/unweighted edges according to a flexible policy
formulated by a neuroscientist. The empirical evaluations show that our
proposed GNN-based framework outperforms standard CNN classifiers across ErrP,
and RSVP datasets, as well as allowing neuroscientific interpretability and
explainability to deep learning methods tailored to EEG related classification
problems. Another practical advantage of our GNN-based framework is that it can
be used in EEG channel selection, which is critical for reducing computational
cost, and designing portable EEG headsets.
