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We present a new dataset and benchmark with the goal of advancing research inthe 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 differentexperimental paradigms. Using this dataset, we also propose a benchmark toevaluate gaze prediction from EEG measurements. The benchmark consists of threetasks with an increasing level of difficulty: left-right, angle-amplitude andabsolute position. We run extensive experiments on this benchmark in order toprovide solid baselines, both based on classical machine learning models and onlarge neural networks. We release our complete code and data and provide asimple and easy-to-use interface to evaluate new methods.

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