  We consider the problem of extracting features from passive, multi-channel
electroencephalogram (EEG) devices for downstream inference tasks related to
high-level mental states such as stress and cognitive load. Our proposed method
leverages recently developed multi-graph tools and applies them to the time
series of graphs implied by the statistical dependence structure (e.g.,
correlation) amongst the multiple sensors. We compare the effectiveness of the
proposed features to traditional band power-based features in the context of
three classification experiments and find that the two feature sets offer
complementary predictive information. We conclude by showing that the
importance of particular channels and pairs of channels for classification when
using the proposed features is neuroscientifically valid.
