The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data
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Date
2023-01-12
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Journal Article
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yes
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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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published
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Volume
13
Pages / Article No.
1028824
Publisher
Frontiers Media
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Subject
Reading task classification; Eye-tracking; EEG; Machine learning; Reading research; Cross-subject evaluation