ATGnet: Adaptive Temporal Graph Network for EEG-enabled Sound Source Tracking in Cocktail Party Scenarios

Abstract

Decoding selective auditory attention from electroencephalography (EEG) signals has gained considerable interest. However, few studies have looked into tracking the dynamic trajectory of moving sound source in complex auditory environments, e.g. with multiple moving speakers. We propose a novel model, namely Adaptive Temporal Graph Network (ATGnet), to continuously track the sound source trajectory using spatial-temporal EEG representations. ATGnet incorporates an adaptive graph topology to extract spatial features, and a graph-convolutional long short-term memory (GC-LSTM) network to capture spatial-temporal dependency. We evaluated ATGnet by performing within-subject leave-one-trial-out cross-validation on EEG signals from 10 participants. Experiment results indicate that ATGnet effectively overcomes the variation of signals across trials and subjects. They further confirm that ATGnet robustly tracks both attended and unattended sound sources, and significantly outperforms traditional methods. ATGnet offers a promising solution to continuous sound source tracking in dynamic conditions, with potential applications in neuro-steered hearing devices.