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arXiv · 2112.06310

Reading Task Classification Using EEG and Eye-Tracking Data

Nora Hollenstein·Marius Tröndle·Martyna Plomecka·Samuel Kiegeland·Yilmazcan Özyurt·Lena A. Jäger·Nicolas Langer
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Abstract

The Zurich Cognitive Language Processing Corpus (ZuCo) provides eye-tracking and EEG signals from two reading paradigms, normal reading and task-specific reading. We analyze whether machine learning methods are able to classify these two tasks using eye-tracking and EEG features. We implement models with aggregated sentence-level features as well as fine-grained word-level features. We test the models in within-subject and cross-subject evaluation scenarios. All models are tested on the ZuCo 1.0 and ZuCo 2.0 data subsets, which are characterized by differing recording procedures and thus allow for different levels of generalizability. Finally, we provide a series of control experiments to analyze the results in more detail.

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Nora Hollenstein, Marius Tröndle, Martyna Plomecka, Samuel Kiegeland, Yilmazcan Özyurt, Lena A. Jäger, Nicolas Langer. 2021-12-12. Reading Task Classification Using EEG and Eye-Tracking Data. https://arxiv.org/abs/2112.06310

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