โก Quick Summary
This study presents an open-source dataset comprising EEG, ERP, and cognitive assessments from 100 Iranian children aged 6-11 years. The research aims to enhance diagnostic precision and intervention efficacy for specific learning disabilities (SLD) through the application of machine learning techniques.
๐ Key Details
- ๐ Dataset: 100 non-clinical Iranian participants, ages 6-11 (Meanโ=โ8.52โยฑโ1.5โSD)
- ๐งฉ Features used: EEG, ERP, cognitive assessments (Raven Test, IVA-2)
- โ๏ธ Technology: Machine learning aligned with the Research Domain Criteria (RDoC) framework
- ๐ง Cognitive assessments: Non-verbal intelligence, attention, working memory tasks
๐ Key Takeaways
- ๐ Comprehensive dataset provides insights into neurodevelopmental profiles in children.
- ๐ก EEG and ERP measures are utilized to explore cognitive functions.
- ๐ฉโ๐ฌ Data collection includes demographic information such as age, gender, and parental history of learning difficulties.
- ๐ Aligns with RDoC framework to improve diagnostic precision for SLD.
- ๐ Valuable resource for researchers studying cognitive development in typically developing children.
- ๐ Potential for machine learning to enhance intervention strategies for learning disabilities.
๐ Background
Understanding the neurodevelopmental profiles of children is crucial for identifying and addressing specific learning disabilities (SLD). Traditional assessment methods often lack the precision needed for effective diagnosis and intervention. This study aims to bridge that gap by providing a dataset that combines neurophysiological measures with cognitive assessments, offering a more comprehensive view of cognitive development in children.
๐๏ธ Study
The dataset was collected from a sample of 100 Iranian children aged 6-11 years, as part of a larger longitudinal study. Participants underwent a series of assessments, including EEG recordings during resting states and cognitive tasks, to capture brain activity associated with working memory and attention. This approach allows researchers to investigate the neural correlates of cognitive functions in typically developing children.
๐ Results
The dataset reveals significant insights into the neurophysiological correlates of cognitive functions. By employing machine learning techniques, researchers can analyze the EEG and ERP data to identify patterns that may be indicative of learning difficulties. This innovative approach holds promise for enhancing the understanding of cognitive development and the early identification of SLD.
๐ Impact and Implications
The implications of this study are profound. By providing a rich dataset that integrates EEG, ERP, and cognitive assessments, researchers can advance the field of neurodevelopmental research. This dataset not only aids in the identification of learning disabilities but also enhances the potential for developing targeted interventions, ultimately improving educational outcomes for children with SLD.
๐ฎ Conclusion
This study highlights the importance of integrating neurophysiological measures with cognitive assessments to better understand cognitive development in children. The open-source dataset serves as a valuable resource for researchers and practitioners alike, paving the way for future studies aimed at improving diagnostic and intervention strategies for learning disabilities. The future of cognitive research looks promising with such innovative approaches!
๐ฌ Your comments
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Iranian 6-11 years age population-based EEG, ERP, and cognition dataset.
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, Meanโ=โ8.52โยฑโ1.5โSD). 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.
Author: [‘Nazari MA’, ‘Abbasi S’, ‘Rezaeian M’, ‘Heysieattalab S’, ‘Safakheil H’, ‘Nasrabadi AM’, ‘Barzegar Z’, ‘Joghataei MT’, ‘Asgharian Z’, ‘Ghobadzadeh F’, ‘Alizadeh M’, ‘Amini Yeganeh P’, ‘Khayyat Naghadehi A’, ‘Azizi K’, ‘Alizadeh Chakharlou M’, ‘Nasiri A’, ‘Davoudkhani M’, ‘Rezaeian M’, ‘Safakheil M’, ‘Katebi A’, ‘Hasanzadeh Tahraband M’, ‘Delkhahi S’, ‘Soltani H’, ‘Shahrabi Farahani V’, ‘Ghasemkhani K’, ‘Nazari E’, ‘Farkhondeh Tale Navi F’]
Journal: Sci Data
Citation: Nazari MA, et al. Iranian 6-11 years age population-based EEG, ERP, and cognition dataset. Iranian 6-11 years age population-based EEG, ERP, and cognition dataset. 2025; 12:319. doi: 10.1038/s41597-025-04624-6