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

Area of Science:

• Cognitive Science
• Neuroscience
• Computational Linguistics

Background:

• Understanding human language processing requires analyzing brain activity and eye movements.
• Existing datasets often lack multimodal recordings for naturalistic reading tasks.

Purpose of the Study:

• To introduce the Zurich Cognitive Language Processing Corpus (ZuCo).
• To provide a rich dataset combining electroencephalography (EEG) and eye-tracking for NLP and cognitive science research.

Main Methods:

• Collected high-density EEG and eye-tracking data from 12 healthy adults.
• Subjects read natural English text for 4-6 hours across various reading tasks.
• The dataset includes recordings for 21,629 words and 154,173 fixations.

Main Results:

• The ZuCo corpus provides synchronized EEG and eye-tracking data during natural sentence reading.
• The dataset covers extensive reading material, suitable for detailed analysis of language comprehension.
• It offers a unique resource for correlating neural and oculomotor signals with linguistic processing.

Conclusions:

• ZuCo is a valuable resource for advancing natural language processing (NLP) and cognitive neuroscience.
• The dataset can be used to train improved machine learning models for information extraction tasks.
• It facilitates research into the neural and behavioral mechanisms of human reading and language understanding.