  Recent behavioral and electroencephalograph (EEG) studies have defined ways
that auditory spatial attention can be allocated over large regions of space.
As with most experimental studies, behavior EEG was averaged over 10s of
minutes because identifying abstract feature spatial codes from raw EEG data is
extremely challenging. The goal of this study is to design a deep learning
model that can learn from raw EEG data and predict auditory spatial information
on a trial-by-trial basis. We designed a convolutional neural networks (CNN)
model to predict the attended location or other stimulus locations relative to
the attended location. A multi-task model was also used to predict the attended
and stimulus locations at the same time. Based on the visualization of our
models, we investigated features of individual classification tasks and joint
feature of the multi-task model. Our model achieved an average 72.4% in
relative location prediction and 90.0% in attended location prediction
individually. The multi-task model improved the performance of attended
location prediction by 3%. Our results suggest a strong correlation between
attended location and relative location.
