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In this study, we validate the findings of previously published papers,showing the feasibility of an Electroencephalography (EEG) based gazeestimation. Moreover, we extend previous research by demonstrating that withonly a slight drop in model performance, we can significantly reduce the numberof electrodes, indicating that a high-density, expensive EEG cap is notnecessary for the purposes of EEG-based eye tracking. Using data-drivenapproaches, we establish which electrode clusters impact gaze estimation andhow the different types of EEG data preprocessing affect the models'performance. Finally, we also inspect which recorded frequencies are mostimportant for the defined tasks.

Electrode Clustering and Bandpass Analysis of EEG Data for Gaze Estimation