Dataset on the EEG time-frequency representation in children with different levels of mathematical achievement

This article presents the data related to the research paper entitled “The analysis of EEG coherence reflects middle childhood differences in mathematical achievement” (González-Garrido et al., 2018). The dataset is derived from the electroencephalographic (EEG) records registered from a total of 60...

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Detalles Bibliográficos
Autores principales: González-Garrido, Andrés A., Gómez-Velázquez, Fabiola R., Romo-Vázquez, Rebeca, Vélez-Pérez, Hugo, Salido-Ruiz, Ricardo A., Espinoza-Valdez, Aurora, Gallardo-Moreno, Geisa B., Ruiz-Stovel, Vanessa D., Martínez-Ramos, Alicia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Linguistics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6226595/
https://www.ncbi.nlm.nih.gov/pubmed/30450402
http://dx.doi.org/10.1016/j.dib.2018.10.105
Descripción
Sumario:This article presents the data related to the research paper entitled “The analysis of EEG coherence reflects middle childhood differences in mathematical achievement” (González-Garrido et al., 2018). The dataset is derived from the electroencephalographic (EEG) records registered from a total of 60 8–9-years-old children with different math skill levels (High: HA, Average: AA, and Low Achievement: LA) while performing a symbolic magnitude comparison task. The average brain patterns are shown through Time-Frequency Representations (TFR) for each group, and also grand-mean amplitudes within specific EEG epochs in a 19-electrode array are provided. Making this information publicly available for further analyses could significantly contribute to a better understanding on how math achievement in children associates with cognitive processing strategies.

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