Auditory spatial attention detection (ASAD) is used to determine the
direction of a listener's attention to a speaker by analyzing her/his
electroencephalographic (EEG) signals. This study aimed to further improve the
performance of ASAD with a short decision window (i.e., <1 s) rather than with
long decision windows in previous studies. An end-to-end temporal attention
network (i.e., TAnet) was introduced in this work. TAnet employs a multi-head
attention (MHA) mechanism, which can more effectively capture the interactions
among time steps in collected EEG signals and efficiently assign corresponding
weights to those EEG time steps. Experiments demonstrated that, compared with
the CNN-based method and recent ASAD methods, TAnet provided improved decoding
performance in the KUL dataset, with decoding accuracies of 92.4% (decision
window 0.1 s), 94.9% (0.25 s), 95.1% (0.3 s), 95.4% (0.4 s), and 95.5% (0.5 s)
with short decision windows (i.e., <1 s). As a new ASAD model with a short
decision window, TAnet can potentially facilitate the design of EEG-controlled
intelligent hearing aids and sound recognition systems.

该研究提出了一种短决策窗口的新型听觉空间注意力检测模型 TAnet，通过使用多头注意力机制更有效地捕捉脑电信号的时间步间相互作用，并为这些时间步分配相应的权重，进一步提高了听觉空间注意力检测的性能。实验证明，相比于基于 CNN 的方法和最新的 ASAD 方法，在 KUL 数据集中，使用短决策窗口时，TAnet 表现出更好的解码性能，准确率分别为 92.4%（决策窗口为 0.1 秒）、94.9%（0.25 秒）、95.1%（0.3 秒）、95.4%（0.4 秒）和 95.5%（0.5 秒）。作为一种具有短决策窗口的新型 ASAD 模型，TAnet 有望促进脑电控制智能助听器和声音识别系统的设计。