ArticleFrontiers in psychology2022
The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data.
Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- The current status of foundation models in decoding inner speech from non-invasive brain signals: a mini review.Frontiers in human neuroscience · 2026Review
- The HeCz corpus: A large, richly annotated reading corpus of newspaper headlines in Czech.Behavior research methods · 2025Article
- A Comprehensive Framework for Eye Tracking: Methods, Tools, Applications, and Cross-Platform Evaluation.Journal of eye movement research · 2025Review
- Comparing human text classification performance and explainability with large language and machine learning models using eye-tracking.Scientific reports · 2024Article
- The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data.Frontiers in psychology · 2022Article
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7 authors.
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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.
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