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Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies

We have developed a new methodology for examining and extracting patterns from brain electric activity by using data mining and machine learning techniques. Data was collected from experiments focused on the study of cognitive processes that might evoke different specific strategies in the resolutio...

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Detalles Bibliográficos
Autores principales: Bosch, Paul, Herrera, Mauricio, López, Julio, Maldonado, Sebastián
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5835340/
https://www.ncbi.nlm.nih.gov/pubmed/29670667
http://dx.doi.org/10.1155/2018/4638903
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author Bosch, Paul
Herrera, Mauricio
López, Julio
Maldonado, Sebastián
author_facet Bosch, Paul
Herrera, Mauricio
López, Julio
Maldonado, Sebastián
author_sort Bosch, Paul
collection PubMed
description We have developed a new methodology for examining and extracting patterns from brain electric activity by using data mining and machine learning techniques. Data was collected from experiments focused on the study of cognitive processes that might evoke different specific strategies in the resolution of math problems. A binary classification problem was constructed using correlations and phase synchronization between different electroencephalographic channels as characteristics and, as labels or classes, the math performances of individuals participating in specially designed experiments. The proposed methodology is based on using well-established procedures of feature selection, which were used to determine a suitable brain functional network size related to math problem solving strategies and also to discover the most relevant links in this network without including noisy connections or excluding significant connections.
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spelling pubmed-58353402018-04-18 Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies Bosch, Paul Herrera, Mauricio López, Julio Maldonado, Sebastián Behav Neurol Research Article We have developed a new methodology for examining and extracting patterns from brain electric activity by using data mining and machine learning techniques. Data was collected from experiments focused on the study of cognitive processes that might evoke different specific strategies in the resolution of math problems. A binary classification problem was constructed using correlations and phase synchronization between different electroencephalographic channels as characteristics and, as labels or classes, the math performances of individuals participating in specially designed experiments. The proposed methodology is based on using well-established procedures of feature selection, which were used to determine a suitable brain functional network size related to math problem solving strategies and also to discover the most relevant links in this network without including noisy connections or excluding significant connections. Hindawi 2018-01-11 /pmc/articles/PMC5835340/ /pubmed/29670667 http://dx.doi.org/10.1155/2018/4638903 Text en Copyright © 2018 Paul Bosch et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Bosch, Paul
Herrera, Mauricio
López, Julio
Maldonado, Sebastián
Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies
title Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies
title_full Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies
title_fullStr Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies
title_full_unstemmed Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies
title_short Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies
title_sort mining eeg with svm for understanding cognitive underpinnings of math problem solving strategies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5835340/
https://www.ncbi.nlm.nih.gov/pubmed/29670667
http://dx.doi.org/10.1155/2018/4638903
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