Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data
This work addresses gaze prediction for applications needing alternatives to video-based eye-tracking, but it appears incremental as it builds on existing CNN-Vision-Transformer methods.
The paper tackled EEG-based gaze prediction by improving the root mean-squared-error to 53.06 millimeters and reducing training time to less than 33% of the original duration.
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