SSAAD: A Multi-Scale Temporal-Frequency Graph Network for Binary Auditory Attention Detection with Self-Supervised Learning

Abstract

Auditory attention detection (AAD) from electroencephalography (EEG) signals has garnered significant interest for its potential in brain-computer interfaces and hearing aids. Nevertheless, accurate decoding remains challenging due to the high-dimensional, non-stationary, and inherently noisy characteristics of EEG signals. We introduce SSAAD, a novel self-supervised approach for binary AAD with three key innovations: (1) A multi-scale temporal-frequency graph convolutional network (MST-GCN) is developed to effectively capture both spatial and temporal EEG dynamics; (2) A hierarchical self-supervised contrastive learning method is designed to cultivate robust EEG representations without extensive labeled data; (3) A bespoke triple-stage mixup data augmentation strategy is proposed to enhance model generalization. Additionally, MST-GCN initialization is achieved via EEG autoencoder pre-training, facilitating superior feature extraction. The method's effectiveness is evaluated on the KUL and DTU datasets, demonstrating state-of-the-art performance with accuracy improvements of 1.4% and 1.7% for 0.1-second windows, respectively.