  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
