Electrode Clustering and Bandpass Analysis of EEG Data for Gaze Estimation
This work addresses cost reduction for EEG-based eye tracking, but it is incremental as it builds on prior research.
The study validated EEG-based gaze estimation feasibility and showed that reducing electrode count slightly lowers model performance, enabling cheaper setups.
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.