EEG-EyeTrack: A Benchmark for Time Series and Functional Data Analysis with Open Challenges and Baselines
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 35 references that are in the map. First posted on arXiv 2 Apr 2025.
Builds on
8 of 35 references.
References · 8
- 2021EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Predictioncited by 21
- 2021Modern Non-Linear Function-on-Function Regressioncited by 11
- 2023Functional Neural Networks: Shift invariant models for functional data with applications to EEG classificationcited by 9
- 2018Detecting deviations from second-order stationarity in locally stationary functional time seriescited by 3
- 2020A Portmanteau-type test for detecting serial correlation in locally stationary functional time seriescited by 3
- 2023One step closer to EEG based eye trackingcited by 3
- 2024GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysiscited by 2
- 2025Consumer-grade EEG-based Eye Trackingcited by 1
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Citing papers per year: 2026: 1.
1 paper.