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Auditory attention is a selective type of hearing in which people focus theirattention intentionally on a specific source of a sound or spoken words whilstignoring or inhibiting other auditory stimuli. In some sense, the auditoryattention score of an individual shows the focus the person can have inauditory tasks. The recent advancements in deep learning and in thenon-invasive technologies recording neural activity beg the question, can deeplearning along with technologies such as electroencephalography (EEG) be usedto predict the auditory attention score of an individual? In this paper, wefocus on this very problem of estimating a person's auditory attention levelbased on their brain's electrical activity captured using 14-channeled EEGsignals. More specifically, we deal with attention estimation as a regressionproblem. The work has been performed on the publicly available Phyaat dataset.The concept of Gramian Angular Difference Field (GADF) has been used to converttime-series EEG data into an image having 14 channels, enabling us to trainvarious deep learning models such as 2D CNN, 3D CNN, and convolutionalautoencoders. Their performances have been compared amongst themselves as wellas with the work done previously. Amongst the different models we tried, 2D CNNgave the best performance. It outperformed the existing methods by a decentmargin of 0.22 mean absolute error (MAE).

Deep Neural Networks on EEG Signals to Predict Auditory Attention Score Using Gramian Angular Difference Field