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EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures

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HCI International 2024 – Late Breaking Papers (HCII 2024)

Abstract

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 performs at a level comparable to the previous State-of-the-Art (SOTA) on the EEGEyeNet Absolute Position Task, achieving a Root Mean Squared Error (RMSE) of 53.6, a 3% reduction in accuracy, 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.

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Correspondence to Teng Liang .

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Liang, T., Damoah, A. (2025). EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures. In: Kurosu, M., Hashizume, A., Mori, H., Asahi, Y., Schmorrow, D.D., Fidopiastis, C.M. (eds) HCI International 2024 – Late Breaking Papers. HCII 2024. Lecture Notes in Computer Science, vol 15374. Springer, Cham. https://doi.org/10.1007/978-3-031-76803-3_20

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  • DOI: https://doi.org/10.1007/978-3-031-76803-3_20

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