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Auditory spatial attention detection (ASAD) aims to decode the attendedspatial location with EEG in a multiple-speaker setting. ASAD methods areinspired by the brain lateralization of cortical neural responses during theprocessing of auditory spatial attention, and show promising performance forthe task of auditory attention decoding (AAD) with neural recordings. In theprevious ASAD methods, the spatial distribution of EEG electrodes is not fullyexploited, which may limit the performance of these methods. In the presentwork, 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 deepconvolutional neural network (DenseNet-3D) is used to extract temporal andspatial features of the neural representation for the attended locations. Theresults show that the proposed method achieves higher decoding accuracy thanthe state-of-the-art (SOTA) method (94.3% compared to XANet's 90.6%) with1-second decision window for the widely used KULeuven (KUL) dataset, and thecode to implement our work is available on Github:  https://github.com/xuxiran/ASAD_DenseNet

A DenseNet-based method for decoding auditory spatial attention with EEG