EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures
<|reference_start|>EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures: Electroencephalography (EEG) analysis is an important domain in the realm of Brain-Computer Interface (BCI) research. To ensure BCI devices are capable of providing practical applications in the real world, brain signal processing techniques must be fast, accurate, and resource-conscious to deliver low-latency neural analytics. This study presents a model that leverages a pre-trained MobileViT alongside Knowledge Distillation (KD) for EEG regression tasks. Our results showcase that this model is capable of performing at a level comparable (only 3% lower) to the previous State-Of-The-Art (SOTA) on the EEGEyeNet Absolute Position Task while being 33% faster and 60% smaller. Our research presents a cost-effective model applicable to resource-constrained devices and contributes to expanding future research on lightweight, mobile-friendly models for EEG regression.<|reference_end|>
@article{liang2024eegmobile:,
    title={EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction
  with Advanced Mobile Architectures},
    author={Teng Liang, Andrews Damoah},
    journal={arXiv preprint arXiv:2408.03449},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03449},
    primaryClass={eess.SP cs.AI cs.LG}
}