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.

翻译：本研究验证了先前发表论文的结论，证明基于脑电图（EEG）的视线估算是可行的。此外，我们通过实验证明，在模型性能仅略有下降的情况下，可以显著减少电极数量，这表明对于基于脑电图（EEG）的眼动追踪而言，高密度、昂贵的脑电帽并非必需。我们采用数据驱动方法，确定了哪些电极聚类会影响视线估计，以及不同类型的脑电图（EEG）数据预处理如何影响模型性能。最后，我们还分析了哪些记录频段对定义的任务最为重要。