Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Label Accuracy in Electronic Health Records and Its Impact on Machine Learning Models for Early Prediction of Gestational Diabetes: 3-Step Retrospective Validation Study.

JMIR medical informatics·2025
Same author

An investigation of pre-stimulus eeg for prediction of driver reaction time.

Biomedical physics & engineering express·2025
Same author

From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces.

Journal of neural engineering·2024
Same author

Daily Activity Lifelogs of People With Heart Failure: Observational Study.

JMIR formative research·2024
Same author

Expert Consensus on the Resource Needs of Autistic Children and Young People in Ireland During the Early Months of the COVID-19 Pandemic.

Journal of developmental and physical disabilities·2023
Same author

Now you see it! Using wearable cameras to gain insights into the lived experience of cardiovascular conditions.

European journal of cardiovascular nursing·2022

Related Experiment Video

Updated: Jun 11, 2025

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG
12:48

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Published on: June 27, 2011

17.8K

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.

More Related Videos

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

25.7K
Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
05:54

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

6.1K

Related Experiment Videos

Last Updated: Jun 11, 2025

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG
12:48

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Published on: June 27, 2011

17.8K
Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

25.7K
Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
05:54

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

6.1K

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.