Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data

Chuhui Qiu, Bugao Liang, Matthew L Key
8/6/2024
cs.LG

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Abstract

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

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Cite this paper

@article{qiu2024effect,
  title  = {Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data},
  author = {Chuhui Qiu and Bugao Liang and Matthew L Key},
  year   = {2024},
  eprint = {2408.03478},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.03478v1}
}

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