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.

本文利用同时测量的脑电图 (EEG) 和眼动数据集，提出了一种可解释的基于关注机制的深度学习框架用于估计眼球注视方向，通过对结果的分析可见该方法在准确性和鲁棒性方面优于现有方法，并提供了解释和强调注意力机制在 EEG 数据分析中提高效率和有效性的潜力。