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Updated: Jun 11, 2025

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DERCo: A Dataset for Human Behaviour in Reading Comprehension Using EEG.

Boi Mai Quach1,2, Cathal Gurrin3,4, Graham Healy3,4

  • 1School of Computing, Dublin City University, Dublin, Ireland. mai.quach3@mail.dcu.ie.

Scientific Data
|October 9, 2024
PubMed
Summary
This summary is machine-generated.

This study presents the Dublin EEG-based Reading Experiment Corpus (DERCo), integrating electroencephalography (EEG) and next-word prediction data. Findings show significant differences in brain activity between high and low predictable words during reading.

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Area of Science:

  • Neuroscience
  • Computational Linguistics
  • Psycholinguistics

Background:

  • Understanding how the brain processes language and predicts upcoming words is crucial.
  • Existing datasets often lack integrated measures of neural activity and large-scale behavioral prediction.
  • The Dublin EEG-based Reading Experiment Corpus (DERCo) addresses this gap.

Purpose of the Study:

  • To introduce and describe the DERCo, a novel dataset combining electroencephalography (EEG) and next-word prediction.
  • To provide a resource for investigating semantic context effects and word predictability in reading.
  • To validate behavioral next-word predictability with neural data.

Main Methods:

  • Collected behavioral data from 500 participants on Amazon Mechanical Turk for next-word prediction.
  • Acquired EEG recordings from 22 healthy adult native English speakers reading narrative texts.
  • Calculated cloze probabilities for words to quantify predictability and analyzed EEG data based on these measures.

Main Results:

  • EEG analyses revealed significant differences in brain activity between high and low predictable words.
  • Demonstrated the utility of integrating behavioral prediction data with neural recordings.
  • Established a correlation between word predictability and neural responses during reading.

Conclusions:

  • The DERCo is a valuable resource for neurolinguistics, enabling the study of context-based word processing.
  • The findings highlight the brain's sensitivity to word predictability in naturalistic reading.
  • This integrated dataset facilitates deeper insights into the neural mechanisms of language comprehension.