biostudies-literatureUnknown21Gonzalez-Garrido AAUniversidad de GuadalajaraThis 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 Data in brief1071-1075https://www.ebi.ac.uk/biostudies/studies/S-EPMC6226595biostudies-literatureDataset on the EEG time-frequency representation in children with different levels of mathematical achievement.PMC6226595Gonzalez-Garrido AAGallardo-Moreno GBGomez-Velazquez FRRomo-Vazquez RSalido-Ruiz RAEspinoza-Valdez AMartinez-Ramos AVelez-Perez HRuiz-Stovel VDfalseDataset 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 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 2018-01-01T00:00:00Z2018 Dec2026-07-16T19:24:08.359Z2026-07-09T13:16:11.115ZS-EPMC62265953045040210.1016/j.dib.2018.10.105