  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 (94.3% 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
