We present a new dataset and benchmark with the goal of advancing research in
the intersection of brain activities and eye movements. Our dataset, EEGEyeNet,
consists of simultaneous Electroencephalography (EEG) and Eye-tracking (ET)
recordings from 356 different subjects collected from three different
experimental paradigms. Using this dataset, we also propose a benchmark to
evaluate gaze prediction from EEG measurements. The benchmark consists of three
tasks with an increasing level of difficulty: left-right, angle-amplitude and
absolute position. We run extensive experiments on this benchmark in order to
provide solid baselines, both based on classical machine learning models and on
large neural networks. We release our complete code and data and provide a
simple and easy-to-use interface to evaluate new methods.

本论文提出了一个新的数据集和基准，旨在推进大脑活动和眼动交叉领域的研究。使用该数据集，我们还提出了一个 EEG 测量下注视预测的基准，并对此进行了广泛的实验，为基础机器学习模型和大型神经网络提供了坚实的基础。