Electroencephalography-based eye tracking (EEG-ET) leverages eye movement artifacts in EEG signals as an alternative to camera-based tracking. While EEG-ET offers advantages such as robustness in low-light conditions and better integration with brain-computer interfaces, its development lags behind traditional methods, particularly in consumer-grade settings. To support research in this area, we present a dataset comprising simultaneous EEG and eye-tracking recordings from 113 participants across 116 sessions, amounting to 11 hours and 45 minutes of recordings. Data was collected using a consumer-grade EEG headset and webcam-based eye tracking, capturing eye movements under four experimental paradigms with varying complexity. The dataset enables the evaluation of EEG-ET methods across different gaze conditions and serves as a benchmark for assessing feasibility with affordable hardware. Data preprocessing includes handling of missing values and filtering to enhance usability. In addition to the dataset, code for data preprocessing and analysis is available to support reproducibility and further research.

翻译：基于脑电图的眼动追踪（EEG-ET）利用脑电信号中的眼动伪影作为基于摄像头的眼动追踪的替代方案。尽管EEG-ET在低光照条件下具有鲁棒性、并能更好地与脑机接口集成等优势，但其发展仍滞后于传统方法，尤其是在消费级应用场景中。为支持该领域的研究，我们提出了一个数据集，包含来自113名参与者在116次实验中的同步脑电与眼动追踪记录，总计11小时45分钟的录制时长。数据采集使用消费级脑电头戴设备和基于网络摄像头的眼动追踪系统，在四种不同复杂度的实验范式下捕获眼动。该数据集支持在不同注视条件下评估EEG-ET方法，并为评估使用经济型硬件的可行性提供了基准。数据预处理包括缺失值处理和滤波以提升可用性。除数据集外，我们还提供了数据预处理与分析代码，以支持研究的可复现性与进一步探索。