  In this study, we validate the findings of previously published papers,
showing the feasibility of an Electroencephalography (EEG) based gaze
estimation. Moreover, we extend previous research by demonstrating that with
only a slight drop in model performance, we can significantly reduce the number
of electrodes, indicating that a high-density, expensive EEG cap is not
necessary for the purposes of EEG-based eye tracking. Using data-driven
approaches, we establish which electrode clusters impact gaze estimation and
how the different types of EEG data preprocessing affect the models'
performance. Finally, we also inspect which recorded frequencies are most
important for the defined tasks.
