用廉价脑电设备实现眼动追踪，支持低成本脑机接口研究
Consumer-grade EEG-based Eye Tracking
- 用消费级脑电设备同步采集脑电与眼动数据，构建新数据集
- 覆盖113人、116次实验，总时长超11小时，涵盖四种复杂度场景
- 开源预处理代码与数据，助力低成本眼动追踪算法验证
基于脑电的眼动追踪（EEG-ET）利用脑电信号中的眼动伪影，作为摄像头方法的替代方案。尽管其在低光环境下更鲁棒且易于与脑机接口集成，但该技术在消费级设备上的发展仍落后于传统方法。为此，我们发布了一个包含113名参与者、116次会话的数据集，总时长达11小时45分钟，采用消费级脑电头戴设备与基于网络摄像头的眼动追踪系统同步采集数据。实验涵盖四种不同复杂度的范式，记录了多种注视条件下的眼动信号。数据经过缺失值处理与滤波等预处理，提升了可用性。此外，配套提供数据预处理与分析代码，确保研究可复现性，为低成本硬件实现的EEG-ET方法评估提供基准。
原文摘要 · Abstract (English)
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.
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