AUTHOR=Hollenstein Nora , Tröndle Marius , Plomecka Martyna , Kiegeland Samuel , Özyurt Yilmazcan , Jäger Lena A. , Langer Nicolas TITLE=The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data JOURNAL=Frontiers in Psychology VOLUME=13 YEAR=2023 URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.1028824 DOI=10.3389/fpsyg.2022.1028824 ISSN=1664-1078 ABSTRACT=

We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading of English sentences. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com.