HeteroEEG: A Dual-Branch Spatial-Spectral-Temporal Heterogeneous Graph Network for EEG Classification.

Abstract

Given the non-Euclidean topology inherent in electroencephalogram (EEG) electrode configurations, graph-based approaches, particularly graph neural networks, have shown notable success across diverse EEG classification tasks. However, since the cerebral cortex lobes function individually and/or collaboratively across diverse tasks, there exist substantial differences between intra-lobe and inter-lobe brain intrinsic functional connectivity. Existing graph networks for EEG classification are based on homogeneous graphs, yet the nature of the cerebral cortex aligns more closely with a heterogeneous graph structure. To this end, we propose HeteroEEG for EEG classification, which to the best of our knowledge is the first to reframe the challenge of exploring EEG spatial information, especially decoupling different types of brain lobes and functional connections, as heterogeneous graph reasoning. Specifically, HeteroEEG is designed to be a dual-branch network aware of spatial, spectral, and temporal EEG features. Experimental results justify the superiority of HeteroEEG in pain and emotion recognition compared with other state-of-the-art studies. The heterogeneous graph construction of HeteroEEG may shed light on future graph-based EEG classification network design.