Electrode Clustering and Bandpass Analysis of EEG Data for Gaze Estimation

Ard Kastrati, Martyna Beata Plomecka, Joël Küchler, Nicolas Langer, Roger Wattenhofer

Keywords: Eye movements, eye tracking, EEG, electrode clustering, bandpass analysis

TL;DR: We investigate which spatio-spectral brain signal components are most relevant for decoding eye movement, what is the minimal and best placement of the electrodes and perform bandpass analysis of EEG data for eye movements

Abstract: 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.

Submission Type: Full Paper

Travel Award - Academic Status: Ph.D. Student

Travel Award - Institution And Country: ETH Zurich, Switzerland

Travel Award - Low To Lower-middle Income Countries: No, my institution does not qualify.