Mental State Classification Using Multi-graph Features [article]

Guodong Chen and Hayden S. Helm and Kate Lytvynets and Weiwei Yang and Carey E. Priebe
2022 arXiv   pre-print
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
more » ... er-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.
arXiv:2203.00516v1 fatcat:txvsywjtafapdpg55erc2mnxgi