The analysis of EEG coherence reflects middle childhood differences in mathematical achievement

González-Garrido, Andrés A.; Gómez-Velázquez, Fabiola R.; Salido-Ruiz, Ricardo A.; Espinoza-Valdez, Aurora; Vélez-Pérez, Hugo; Romo-Vázquez, Rebeca; Gallardo-Moreno, Geisa B.; Ruiz-Stovel, Vanessa D.; Martínez-Ramos, Alicia; Berumen, Gustavo

doi:10.1016/j.bandc.2018.04.006

Cited by 26 publications

(18 citation statements)

References 67 publications

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“…González-Garrido and colleagues reported that middle-childhood individuals with lower arithmetic skills showed significantly higher coherence in the θ and α frequency bands than their more-skilled peers [11]. The interpretation of these findings coincides with what other authors have reported [83,84] by acknowledging a major role of the β band in numericalprocessing, and further suggesting that the number-processing specialization system likely involves several complex, interacting neural networks that require the selection of a cognitive strategy better-suited to individual resource availability.…”

Section: Discussionmentioning

confidence: 99%

“…Data were obtained from [11], which includes a total of 57 children aged 8-11 years. This sample, including both girls and boys, was selected from a pool of 441 third-graders from six public and three private elementary schools.…”

Section: Participants' Datamentioning

confidence: 99%

“…All participants were matched according to their educational environment, such that each LA subject was paired with a child from the same school with AA and HA. This split criterion has been used previously to explore different aspects of neurodevelopment in children with distinct math abilities [11,[24][25][26]. Children with antecedents of neurological, psychiatric or behavioral disorders were not included in the final pool of participants, none of whom had ever repeated a school grade.…”

Section: Participants' Datamentioning

confidence: 99%

“…The four possible outcomes of the predictions are: true positive (TP) and true negative (TN), which are correct classifications; and false positive (FP) and false negative (FN), which are incorrect classifications. Based on these concepts, it is possible to compute the performance rate, as expressed in Eq (11).…”

Section: Machine-learning Processingmentioning

confidence: 99%

“…Specifically, EEG coherence measures, which represent the degree of association or coupling of frequency spectra between two different time series, have been considered a reflection of integrated cortical functions [3]. EEG coherence measures also represent the functional association between different brain regions [4] and have, therefore, been used to evaluate the neurofunctional substrates underlying the learning of specific math concepts [5], arithmetic operations [6,7], reverse counting [8], proportional reasoning [9], identification of digit sequences [10], and numerical comparison [11] in different populations.…”

Section: Introductionmentioning

confidence: 99%

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A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics

et al. 2020

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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 lowskilled 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.

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Section: Discussionmentioning

confidence: 99%

Section: Participants' Datamentioning

confidence: 99%

Section: Participants' Datamentioning

confidence: 99%

Section: Machine-learning Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics

et al. 2020

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The neural differences of arithmetic verification performance depend on math skill: Evidence from event‐related potential

Taghizadeh

Hashemi

Jahan

et al. 2021

Neuropsychopharm Rep

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Aim Math skill is a basic need for an individual, as a career prospect. However, little is known about early brain processes of arithmetic between individuals with different math skill. Therefore, we questioned the modulation of the amplitude of an early negative component by math skill level in an arithmetic verification paradigm using event‐related potential (ERP). Methods Thirty‐six right‐handed participants were assigned in two groups of high‐ and low‐performing students. Their electroencephalogram was recorded while they completed an arithmetic verification task. Simple arithmetic operands were made by random digits from 1 to 9. Addition and subtraction operations were equally used in correct and incorrect responses. The accuracy scores, reaction times, and peak amplitude of the negativity in 200‐400 ms time window were analyzed. Results The high‐performing group showed significantly higher response speeds, and they were more accurate than the low‐performing group. The group × region interaction effect was significant. The high‐performing group showed a significantly greater negativity, particularly in parietal region, while the low‐performing group showed a significantly deeper negativity in frontal and prefrontal region. In the low‐performing group, there were significant peak amplitude differences between the anterior and posterior areas. However, such differences were not detected in the high‐performing group. Conclusion Students with different mathematical performance showed distinct patterns in early processing of arithmetic verification, as reflected by differences in negativity at 200‐400 ms at anterior and posterior. This suggests that ERPs could be used to differentiate math mastery at neural level which is beneficial in educational and clinical contexts.

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EEG-PML: A Software for Processing and Machine Learning Analysis of EEG Signals

et al. 2019

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The analysis of EEG coherence reflects middle childhood differences in mathematical achievement

Cited by 26 publications

References 67 publications

A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics

A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics

The neural differences of arithmetic verification performance depend on math skill: Evidence from event‐related potential

EEG-PML: A Software for Processing and Machine Learning Analysis of EEG Signals

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