# Electrode Clustering and Bandpass Analysis of EEG Data for Gaze Estimation

arXiv: https://arxiv.org/abs/2302.12710 · HTML: https://mlsanity.com/paper/2302.12710/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 19 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [ICLabel: An automated electroencephalographic independent component classifier, dataset, and website](https://mlsanity.com/paper/1901.07915/) (2019)
- [EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction](https://mlsanity.com/paper/2111.05100/) (2021)
- [A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications](https://mlsanity.com/paper/2206.08672/) (2022)

## Built on by (most cited first, 2 of 2)

- [An Interpretable and Attention-based Method for Gaze Estimation Using Electroencephalography](https://mlsanity.com/paper/2308.05768/) (2023)
- [EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures](https://mlsanity.com/paper/2408.03449/) (2024)

## Abstract (arXiv)

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.