用脑电数据重建眼动，构建新基准并给出可复现基线
EEG-EyeTrack: A Benchmark for Time Series and Functional Data Analysis with Open Challenges and Baselines
- 基于脑电信号重建眼动，提出专用评估指标与挑战任务
- 在消费级硬件上实现基线性能，验证方法可行性
- 开放数据集与代码，适合时序分析与跨模态研究者
本文提出一个用于功能数据分析（FDA）的新基准数据集，聚焦于从脑电（EEG）信号中重建眼动。贡献有二：一是针对FDA应用设计了开放挑战与评估指标；二是采用功能神经网络建立主回归任务的基线结果，即从EEG信号重建眼动。报告了在消费级硬件上的基线表现，并基于研究级硬件的EEGEyeNet数据集进行了对比分析。
原文摘要 · Abstract (English)
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.
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