One step closer to EEG based eye tracking

TL;DR

This paper introduces a novel deep neural network for EEG-based eye tracking, improving gaze estimation accuracy over previous methods but still not yet suitable for practical use.

Contribution

The paper presents a new DNN architecture that exploits spatial dependencies in EEG signals for direct gaze estimation, advancing the state of the art.

Findings

Achieved 3.5 cm reduction in MAE compared to previous methods

Demonstrated the feasibility of EEG-based eye tracking as an alternative to image-based methods

Identified current limitations preventing practical application

Abstract

In this paper, we present two approaches and algorithms that adapt areas of interest We present a new deep neural network (DNN) that can be used to directly determine gaze position using EEG data. EEG-based eye tracking is a new and difficult research topic in the field of eye tracking, but it provides an alternative to image-based eye tracking with an input data set comparable to conventional image processing. The presented DNN exploits spatial dependencies of the EEG signal and uses convolutions similar to spatial filtering, which is used for preprocessing EEG signals. By this, we improve the direct gaze determination from the EEG signal compared to the state of the art by 3.5 cm MAE (Mean absolute error), but unfortunately still do not achieve a directly applicable system, since the inaccuracy is still significantly higher compared to image-based eye trackers. Link: https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FEEGGaze&mode=list