One step closer to EEG based eye tracking
This work addresses the challenge of providing an alternative to image-based eye tracking for applications where EEG data is available, but it is incremental as it builds on existing methods without achieving directly applicable accuracy.
The paper tackled the problem of EEG-based eye tracking by developing a new deep neural network that exploits spatial dependencies in EEG signals, improving gaze position determination by 3.5 cm MAE compared to state-of-the-art methods, though it remains less accurate than image-based systems.
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