An Interpretable and Attention-based Method for Gaze Estimation Using Electroencephalography

TL;DR

This paper introduces an interpretable, attention-based deep learning model for gaze estimation from EEG data, enhancing accuracy and robustness while providing visual explanations of the model's focus areas.

Contribution

The paper presents a novel attention-based deep learning framework that improves gaze estimation from EEG signals and offers interpretability through visualization.

Findings

Outperforms existing methods in accuracy

Demonstrates robustness across datasets

Provides visual explanations of model focus

Abstract

Eye movements can reveal valuable insights into various aspects of human mental processes, physical well-being, and actions. Recently, several datasets have been made available that simultaneously record EEG activity and eye movements. This has triggered the development of various methods to predict gaze direction based on brain activity. However, most of these methods lack interpretability, which limits their technology acceptance. In this paper, we leverage a large data set of simultaneously measured Electroencephalography (EEG) and Eye tracking, proposing an interpretable model for gaze estimation from EEG data. More specifically, we present a novel attention-based deep learning framework for EEG signal analysis, which allows the network to focus on the most relevant information in the signal and discard problematic channels. Additionally, we provide a comprehensive evaluation of the presented framework, demonstrating its superiority over current methods in terms of accuracy and robustness. Finally, the study presents visualizations that explain the results of the analysis and highlights the potential of attention mechanism for improving the efficiency and effectiveness of EEG data analysis in a variety of applications.