TITLE: EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction 

ABSTRACT: 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.
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.
EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction
http://arxiv.org/abs/2111.05100v2