Open AccessA brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics
Author(s) -
Sulema Torres-Ramos,
Ricardo A. Salido-Ruiz,
Aurora Espinoza-Valdez,
Fabiola R. Gómez-Velázquez,
Andrés A. González-Garrido,
Israel Román-Godínez
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0227613
Subject(s) - electroencephalography , computer science , graph , pattern recognition (psychology) , artificial intelligence , decision tree , signal processing , coherence (philosophical gambling strategy) , power graph analysis , machine learning , speech recognition , mathematics , psychology , statistics , theoretical computer science , digital signal processing , neuroscience , computer hardware
Recent studies aiming to facilitate mathematical skill development in primary school children have explored the electrophysiological characteristics associated with different levels of arithmetic achievement. The present work introduces an alternative EEG signal characterization using graph metrics and, based on such features, a classification analysis using a decision tree model. This proposal aims to identify group differences in brain connectivity networks with respect to mathematical skills in elementary school children. The methods of analysis utilized were signal-processing (EEG artifact removal, Laplacian filtering, and magnitude square coherence measurement) and the characterization (Graph metrics) and classification (Decision Tree) of EEG signals recorded during performance of a numerical comparison task. Our results suggest that the analysis of quantitative EEG frequency-band parameters can be used successfully to discriminate several levels of arithmetic achievement. Specifically, the most significant results showed an accuracy of 80.00% ( α band), 78.33% ( δ band), and 76.67% ( θ band) in differentiating high-skilled participants from low-skilled ones, averaged-skilled subjects from all others, and averaged-skilled participants from low-skilled ones, respectively. The use of a decision tree tool during the classification stage allows the identification of several brain areas that seem to be more specialized in numerical processing.