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

Area of Science:

• Neuroscience
• Developmental Psychology
• Cognitive Science

Background:

• Understanding neurodevelopmental profiles in children is crucial for identifying learning disabilities.
• Existing research often lacks comprehensive datasets integrating electrophysiological and cognitive measures.
• The Research Domain Criteria (RDoC) framework provides a valuable structure for investigating mental function.

Purpose of the Study:

• To present an open-source dataset of electroencephalography (EEG), event-related potentials (ERPs), and cognitive assessments from typically developing children.
• To facilitate research on the neurophysiological underpinnings of cognitive functions and specific learning disabilities (SLD).
• To support the application of machine learning in enhancing diagnostic precision for SLDs.

Main Methods:

• Collected EEG (resting-state and task-based) and ERP data from 100 Iranian children (aged 6-11).
• Administered cognitive assessments including non-verbal intelligence (Raven Test), attention (IVA-2), and working memory tasks.
• Gathered demographic data, parental history of learning difficulties, and Child Symptom Inventory-4 (CSI-4) scores.

Main Results:

• The dataset includes detailed neurophysiological and cognitive profiles of typically developing children.
• It captures brain activity during resting-state and working memory tasks with varying stimuli.
• The data is suitable for exploring correlations between EEG/ERP measures and cognitive performance.

Conclusions:

• This dataset is a valuable resource for advancing the understanding of cognitive development in children.
• It enables further investigation into the neural correlates of attention, working memory, and intelligence.
• The data can contribute to developing more accurate diagnostic tools and interventions for learning disabilities.