  In this paper, we present an algorithm of gaze prediction from
Electroencephalography (EEG) data. EEG-based gaze prediction is a new research
topic that can serve as an alternative to traditional video-based eye-tracking.
Compared to the existing state-of-the-art (SOTA) method, we improved the root
mean-squared-error of EEG-based gaze prediction to 53.06 millimeters, while
reducing the training time to less than 33% of its original duration. Our
source code can be found at https://github.com/AmCh-Q/CSCI6907Project
