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Eye movements can reveal valuable insights into various aspects of humanmental processes, physical well-being, and actions. Recently, several datasetshave been made available that simultaneously record EEG activity and eyemovements. This has triggered the development of various methods to predictgaze direction based on brain activity. However, most of these methods lackinterpretability, which limits their technology acceptance. In this paper, weleverage a large data set of simultaneously measured Electroencephalography(EEG) and Eye tracking, proposing an interpretable model for gaze estimationfrom EEG data. More specifically, we present a novel attention-based deeplearning framework for EEG signal analysis, which allows the network to focuson the most relevant information in the signal and discard problematicchannels. Additionally, we provide a comprehensive evaluation of the presentedframework, demonstrating its superiority over current methods in terms ofaccuracy and robustness. Finally, the study presents visualizations thatexplain the results of the analysis and highlights the potential of attentionmechanism for improving the efficiency and effectiveness of EEG data analysisin a variety of applications.

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