TAnet: A New Temporal Attention Network for EEG-based Auditory Spatial
  Attention Decoding with a Short Decision Window
Yuting Ding Fei Chen
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.