Iranian 6-11 years age population-based EEG, ERP, and cognition dataset

This report presents an open-source dataset investigating neurodevelopmental profiles in children. The dataset consists of EEG, ERP, and cognitive assessments from 100 Iranian non-clinical participants (age range 6-11 years, Mean = 8.52 ± 1.5 SD). Notably, this is a smaller group drawn from a larger longitudinal ongoing study. The research aligns with the Research Domain Criteria (RDoC) framework, aiming to enhance diagnostic precision and intervention efficacy for specific learning disabilities (SLD) using EEG/ERP measures and machine learning. Cognitive assessments included non-verbal intelligence (Raven Test), attention (IVA-2), and working memory tasks. EEG recordings captured resting-state (eyes closed/open) and brain activity during working memory tasks with numerical and non-numerical stimuli (ERPs). Additionally, demographic information such as age, gender, education, handedness, parental history of learning difficulties, and child symptom inventory-4 (CSI-4) were collected. This dataset provides a valuable resource for exploring the neurophysiological correlates of cognitive functions in typically developing children, which can advance our understanding of the neural foundations of cognitive development in children.

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Publication Details

Journal
Scientific Data
Published
2025-02-22
DOI
https://doi.org/10.1038/s41597-025-04624-6
Citations
3
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
4.29
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Iranian 6-11 years age population-based EEG, ERP, and cognition dataset

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Iranian 6-11 years age population-based EEG, ERP, and cognition dataset

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article en
3 citations

Abstract

This report presents an open-source dataset investigating neurodevelopmental profiles in children. The dataset consists of EEG, ERP, and cognitive assessments from 100 Iranian non-clinical participants (age range 6-11 years, Mean = 8.52 ± 1.5 SD). Notably, this is a smaller group drawn from a larger longitudinal ongoing study. The research aligns with the Research Domain Criteria (RDoC) framework, aiming to enhance diagnostic precision and intervention efficacy for specific learning disabilities (SLD) using EEG/ERP measures and machine learning. Cognitive assessments included non-verbal intelligence (Raven Test), attention (IVA-2), and working memory tasks. EEG recordings captured resting-state (eyes closed/open) and brain activity during working memory tasks with numerical and non-numerical stimuli (ERPs). Additionally, demographic information such as age, gender, education, handedness, parental history of learning difficulties, and child symptom inventory-4 (CSI-4) were collected. This dataset provides a valuable resource for exploring the neurophysiological correlates of cognitive functions in typically developing children, which can advance our understanding of the neural foundations of cognitive development in children.

Scientific DataVol. 12(1)
Shahed University (IR), Islamic Azad University Roudehen Branch (IR), Islamic Azad University South Tehran Branch (IR), Islamic Azad University, Science and Research Branch (IR), Iran University of Medical Sciences (IR), Kansas State University (US), Islamic Azad University, Karaj (IR), University of Tehran (IR), Institute for Cognitive Science Studies (IR), University of Tabriz (IR), Islamic Azad University of Ahar (IR), Shahid Beheshti University (IR), Shahid Beheshti University of Medical Sciences (IR), University of Milano-Bicocca (IT), Rafsanjan University of Medical Sciences (IR)
Quality Education
Openalex Percentile: Top 7%
EEG and Brain-Computer Interfaces
4.29
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