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Auditory spatial attention detection (ASAD) is used to determine thedirection of a listener's attention to a speaker by analyzing her/hiselectroencephalographic (EEG) signals. This study aimed to further improve theperformance of ASAD with a short decision window (i.e., <1 s) rather than withlong decision windows in previous studies. An end-to-end temporal attentionnetwork (i.e., TAnet) was introduced in this work. TAnet employs a multi-headattention (MHA) mechanism, which can more effectively capture the interactionsamong time steps in collected EEG signals and efficiently assign correspondingweights to those EEG time steps. Experiments demonstrated that, compared withthe CNN-based method and recent ASAD methods, TAnet provided improved decodingperformance in the KUL dataset, with decoding accuracies of 92.4% (decisionwindow 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 shortdecision window, TAnet can potentially facilitate the design of EEG-controlledintelligent hearing aids and sound recognition systems.

TAnet: A New Temporal Attention Network for EEG-based Auditory Spatial Attention Decoding with a Short Decision Window