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

arXiv: https://arxiv.org/abs/2110.12503 · HTML: https://mlsanity.com/paper/2110.12503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 24 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Deep Visual Attention Prediction](https://mlsanity.com/paper/1705.02544/) (2017)
- [Classification of Time-Series Images Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1710.00886/) (2017)
- [ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification](https://mlsanity.com/paper/1904.12546/) (2019)
- [Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis](https://mlsanity.com/paper/2003.04534/) (2020)
- [PhyAAt: Physiology of Auditory Attention to Speech Dataset](https://mlsanity.com/paper/2005.11577/) (2020)

## Abstract (arXiv)

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).