Task-related and Resting-state EEG Correlates of Mathematical Skills

Steiner, Helen; Martynova, Olga

doi:10.1109/cnn56452.2022.9912536

Cited by 2 publications

(3 citation statements)

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“…The obtained results are partially consistent with previous findings showing differences in the spectral power of the theta rhythm between AM and BM groups [11]. In this study, a group statistical comparison did not show any between-group difference in restingstate EEG.…”

Section: Discussionsupporting

confidence: 93%

“…One functional magnetic resonance imaging (fMRI) paper examined the topography of functional brain activation, particularly in mathematics professionals [10]. We have also reported that the electroencephalography (EEG) spectral pattern differed between specialists in mathematics and specialists in the humanities [11]; and different EEG patterns were induced by solving verbal and mathematic tasks that made it possible to classify subject-specific EEG [12]. However, while most studies focused on specific EEG patterns [5,9,11] and/or specific brain regions [7,8,10] playing crucial roles in mathematical cognition, only a few explored EEG features in terms of classification of individual cognitive albitites or their prediction in samples of neurotypical populations [13].…”

Section: Introductionmentioning

confidence: 99%

“…We have also reported that the electroencephalography (EEG) spectral pattern differed between specialists in mathematics and specialists in the humanities [11]; and different EEG patterns were induced by solving verbal and mathematic tasks that made it possible to classify subject-specific EEG [12]. However, while most studies focused on specific EEG patterns [5,9,11] and/or specific brain regions [7,8,10] playing crucial roles in mathematical cognition, only a few explored EEG features in terms of classification of individual cognitive albitites or their prediction in samples of neurotypical populations [13]. A powerful approach for early detection of cognitive abilities could be EEG classification using specific EEG pattern recognition by machine learning algorithms [14].…”

Section: Introductionmentioning

confidence: 99%

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Cross-Subject Classification of Effectiveness in Performing Cognitive Tasks Using Resting-State EEG

2023

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A high level of mathematical education is often associated with high effectiveness in solving cognitive problems and professional success. It is known that cognitive processes are accompanied by specific bioelectric activity in the brain and success in mathematical education as a behavioral phenotype is also reflected in EEG both during mental activity and at rest. This study tested the potential to distinguish volunteers with an advanced level of education in mathematics (AM) from individuals with a basic level of education in mathematics (BM) based on the frequency parameters of the resting-state electroencephalogram (EEG) recorded before the start of cognitive tasks. Further, the volunteers were divided into two groups, highly successful and moderately successful, according to their task-solving performance. The Light Gradient Boosting Machine learning algorithm was used for cross-subject classification based on the power spectral density of seven EEG frequency bands. It most accurately recognized and differentiated EEG of highly successful from highly successful BM subjects. The results indicate that success in solving tasks in combination with a high level of education in mathematics can be reflected in or predicted by the specific rhythmic activity of the brain at rest.

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Section: Discussionsupporting

confidence: 93%