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<p>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: <a href="https://github.com/dalin6666/GSANet">https://github.com/dalin6666/GSANet</a>.</p>

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

  • 期刊论文

  • 发表日期：

    2025年11月8日

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  • Yuanlin Dong,
  • Rui Dai,
  • Tiancheng Xie,
  • Ke Xu,
  • Liya Huang

摘要

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.