A new benchmark dataset for functional data analysis (FDA) is presented, focusing on the reconstruction of eye movements from EEG data. The contribution is twofold: first, open challenges and evaluation metrics tailored to FDA applications are proposed. Second, functional neural networks are used to establish baseline results for the primary regression task of reconstructing eye movements from EEG signals. Baseline results are reported for the new dataset, based on consumer-grade hardware, and the EEGEyeNet dataset, based on research-grade hardware.
@article{arxiv.2504.03760,
title = {EEG-EyeTrack: A Benchmark for Time Series and Functional Data Analysis with Open Challenges and Baselines},
author = {Tiago Vasconcelos Afonso and Florian Heinrichs},
journal= {arXiv preprint arXiv:2504.03760},
year = {2025}
}