Attention-guided graph structure learning network for EEG-enabled auditory attention detection

Abstract

Humans possess the remarkable ability to selectively focus on one sound source in a cocktail party scenario. Decoding auditory attention from brain signals is essential for the development of neuro-steered hearing aids. However, it remains challenging to extract discriminative feature representation from electroencephalography (EEG) signals for auditory attention detection (AAD) tasks, and most methods ignore the intrinsic relationship between different EEG channels. To address these challenges, we propose a novel attention-guided graph structure learning network, AGSLnet, which leverages potential relationships between EEG channels to improve AAD performance. Specifically, AGSLnet is designed to dynamically capture latent relationships between channels and construct a graph structure of EEG signals. We evaluated AGSLnet on two publicly available AAD datasets and demonstrated its superiority and robustness over state-of-the-art models. Furthermore, visualization of the graph structure trained by AGSLnet supports previous neuroscience findings, enhancing our understanding of the underlying neural mechanisms.