In this study, we validate the findings of previously published papers,
showing the feasibility of an Electroencephalography (EEG) based gaze
estimation. Moreover, we extend previous research by demonstrating that with
only a slight drop in model performance, we can significantly reduce the number
of electrodes, indicating that a high-density, expensive EEG cap is not
necessary for the purposes of EEG-based eye tracking. Using data-driven
approaches, we establish which electrode clusters impact gaze estimation and
how the different types of EEG data preprocessing affect the models'
performance. Finally, we also inspect which recorded frequencies are most
important for the defined tasks.

本研究验证了以前出版论文的研究结果，并展示了基于脑电图的凝视估计的可行性，通过使用数据驱动方法，我们证明在模型表现略微下降的情况下，可以显著减少电极数量，从而不需要高密度昂贵的 EEG 帽就可以完成 EEG 基础的凝视追踪。我们确定了哪些电极簇对凝视估计有影响，以及不同类型的 EEG 数据预处理对模型性能的影响，并最终检查了记录的频率对所定义任务最重要的方面。