The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data

Author:

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

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.

Publisher

Frontiers Media SA

Subject

General Psychology

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