Geo-GCN: Geometric-Graphical Convolutional Network for EEG-based Auditory Attention Detection

Auditory attention detection (AAD) reveals listeners' attention to a speech stimulus based on their elicited electroencephalography (EEG) signals. We propose a geometric graph convolutional network (Geo-GCN) that uses the physical layout of EEG sensors to construct a distance-based adjacency matrix. This enables Geo-GCN to perform more biologically informed feature learning than standard GCNs. Using data from participants with normal hearing (NH) and hearing-impaired (HI), our method outperforms traditional GCNs. Geo-GCN also demonstrates lower performance variability among participants. Analysis of separate NH and HI groups shows consistent gains over standard GCN, underlining the benefit of explicit modeling of scalp geometry. These findings highlight the potential of geometry-aware graph neural networks to improve EEG-based auditory attention detection, particularly in heterogeneous populations with varied hearing capabilities.