Auditory spatial attention detection (ASAD) aims to decode the attended spatial location with EEG in a multiple-speaker setting. ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings. In the previous ASAD methods, the spatial distribution of EEG electrodes is not fully exploited, which may limit the performance of these methods. In the present work, by transforming the original EEG channels into a two-dimensional (2D) spatial topological map, the EEG data is transformed into a three-dimensional (3D) arrangement containing spatial-temporal information. And then a 3D deep convolutional neural network (DenseNet-3D) is used to extract temporal and spatial features of the neural representation for the attended locations. The results show that the proposed method achieves higher decoding accuracy than the state-of-the-art (SOTA) method (94.4% compared to XANet's 90.6%) with 1-second decision window for the widely used KULeuven (KUL) dataset, and the code to implement our work is available on Github: https://github.com/xuxiran/ASAD_DenseNet

翻译：听觉空间注意力检测（ASAD）旨在多说话人场景下通过脑电图解码被注意的空间位置。ASAD方法受听觉空间注意力处理过程中大脑皮层神经反应偏侧化的启发，在基于神经记录的听觉注意力解码（AAD）任务中展现出良好性能。以往的ASAD方法未能充分利用脑电图电极的空间分布特性，这可能限制了这些方法的性能。本研究通过将原始脑电图通道转换为二维空间拓扑图，将脑电图数据转化为包含时空信息的三维结构，进而采用三维深度卷积神经网络（DenseNet-3D）提取被注意位置神经表征的时空特征。实验结果表明，在广泛使用的KULeuven（KUL）数据集上，本方法在1秒决策窗口下实现了比当前最优方法更高的解码准确率（94.4%对比XANet的90.6%）。本研究的实现代码已发布至GitHub：https://github.com/xuxiran/ASAD_DenseNet