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ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading


Hollenstein, Nora; Rotsztejn, Jonathan; Troendle, Marius; Pedroni, Andreas; Zhang, Ce; Langer, Nicolas (2018). ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading. Scientific Data, 5:180291.

Abstract

We present the Zurich Cognitive Language Processing Corpus (ZuCo), a dataset combining electroencephalography (EEG) and eye-tracking recordings from subjects reading natural sentences. ZuCo includes high-density EEG and eye-tracking data of 12 healthy adult native English speakers, each reading natural English text for 4-6 hours. The recordings span two normal reading tasks and one task-specific reading task, resulting in a dataset that encompasses EEG and eye-tracking data of 21,629 words in 1107 sentences and 154,173 fixations. We believe that this dataset represents a valuable resource for natural language processing (NLP). The EEG and eye-tracking signals lend themselves to train improved machine-learning models for various tasks, in particular for information extraction tasks such as entity and relation extraction and sentiment analysis. Moreover, this dataset is useful for advancing research into the human reading and language understanding process at the level of brain activity and eye-movement.

Abstract

We present the Zurich Cognitive Language Processing Corpus (ZuCo), a dataset combining electroencephalography (EEG) and eye-tracking recordings from subjects reading natural sentences. ZuCo includes high-density EEG and eye-tracking data of 12 healthy adult native English speakers, each reading natural English text for 4-6 hours. The recordings span two normal reading tasks and one task-specific reading task, resulting in a dataset that encompasses EEG and eye-tracking data of 21,629 words in 1107 sentences and 154,173 fixations. We believe that this dataset represents a valuable resource for natural language processing (NLP). The EEG and eye-tracking signals lend themselves to train improved machine-learning models for various tasks, in particular for information extraction tasks such as entity and relation extraction and sentiment analysis. Moreover, this dataset is useful for advancing research into the human reading and language understanding process at the level of brain activity and eye-movement.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Neuroscience Center Zurich
06 Faculty of Arts > Institute of Psychology
08 Research Priority Programs > Dynamics of Healthy Aging
Dewey Decimal Classification:150 Psychology
Language:English
Date:11 December 2018
Deposited On:26 Feb 2019 14:57
Last Modified:26 Feb 2019 14:58
Publisher:Nature Publishing Group
ISSN:2052-4463
Additional Information:Hollenstein N.. et al. . Open Science Framework. 2018 https://doi.org/10.17605/OSF.IO/Q3ZWS
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/sdata.2018.291
PubMed ID:30531985

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