Discovering human cognitive and emotional states using multi-modal
physiological signals draws attention across various research applications.
Physiological responses of the human body are influenced by human cognition and
commonly used to analyze cognitive states. From a network science perspective,
the interactions of these heterogeneous physiological modalities in a graph
structure may provide insightful information to support prediction of cognitive
states. However, there is no clue to derive exact connectivity between
heterogeneous modalities and there exists a hierarchical structure of
sub-modalities. Existing graph neural networks are designed to learn on
non-hierarchical homogeneous graphs with pre-defined graph structures; they
failed to learn from hierarchical, multi-modal physiological data without a
pre-defined graph structure. To this end, we propose a hierarchical
heterogeneous graph generative network (H2G2-Net) that automatically learns a
graph structure without domain knowledge, as well as a powerful representation
on the hierarchical heterogeneous graph in an end-to-end fashion. We validate
the proposed method on the CogPilot dataset that consists of multi-modal
physiological signals. Extensive experiments demonstrate that our proposed
method outperforms the state-of-the-art GNNs by 5%-20% in prediction accuracy.

提出了一种自动学习无领域知识下的层次异构图生成网络（H2G2-Net），以及一种端到端的强大的层次异构图表示方法，用于预测人类的认知状态，并在实验中验证了其在多模态生理信号方面的优越性。