Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data
<|reference_start|>Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data: 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<|reference_end|>
@article{qiu2024effect,
    title={Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction
  Using Electroencephalography Data},
    author={Chuhui Qiu, Bugao Liang, Matthew L Key},
    journal={arXiv preprint arXiv:2408.03478},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03478},
    primaryClass={cs.LG}
}