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

TL;DR

This paper explores using deep learning models on EEG-derived images to predict individual auditory attention scores, demonstrating that 2D CNNs outperform previous methods with improved accuracy.

Contribution

It introduces a novel approach of converting EEG signals into Gramian Angular Difference Field images for attention score regression, outperforming existing techniques.

Findings

2D CNN achieved the lowest MAE among tested models.

The proposed GADF-based method outperformed previous approaches by 0.22 MAE.

Deep learning models can effectively predict auditory attention from EEG data.

Abstract

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).