  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.
