GSANet: research on EEG decoding based on graph attention and self attention in auditory attention detection.

Humans demonstrate the ability to focus auditory attention in noisy environments, enabling them to concentrate on a specific speaker at a cocktail party. Neuroscientific research has shown that auditory attention itself is a dynamic brain activity that evolves over time, which has inspired studies on electroencephalography (EEG)-based auditory attention detection (AAD). This paper proposes a neural attention mechanism model named GSANet, which employs a self-attention mechanism to model the temporal dynamics of EEG signals while dynamically assigning weights to EEG channels through a graph attention mechanism. In brief, GSANet simulates the neural attention mechanisms of the human brain to extract discriminative representations from EEG signals for training high-performance classifiers. We conducted experiments on two public datasets, KUL and DTU, achieving overall decoding accuracies of 94.5% and 79.2%, respectively, under a 1-second decision window, significantly outperforming baseline models across all comparative conditions. The code of our proposed method will be available at: https://github.com/dalin6666/GSANet.