  Auditory attention is a selective type of hearing in which people focus their
attention intentionally on a specific source of a sound or spoken words whilst
ignoring or inhibiting other auditory stimuli. In some sense, the auditory
attention score of an individual shows the focus the person can have in
auditory tasks. The recent advancements in deep learning and in the
non-invasive technologies recording neural activity beg the question, can deep
learning along with technologies such as electroencephalography (EEG) be used
to predict the auditory attention score of an individual? In this paper, we
focus on this very problem of estimating a person's auditory attention level
based on their brain's electrical activity captured using 14-channeled EEG
signals. More specifically, we deal with attention estimation as a regression
problem. The work has been performed on the publicly available Phyaat dataset.
The concept of Gramian Angular Difference Field (GADF) has been used to convert
time-series EEG data into an image having 14 channels, enabling us to train
various deep learning models such as 2D CNN, 3D CNN, and convolutional
autoencoders. Their performances have been compared amongst themselves as well
as with the work done previously. Amongst the different models we tried, 2D CNN
gave the best performance. It outperformed the existing methods by a decent
margin of 0.22 mean absolute error (MAE).
