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Behavioural Neurology
Volume 2018, Article ID 4638903, 15 pages
Research Article

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

1Facultad de Ingeníera, Universidad del Desarrollo, Av. Plaza 700, Las Condes, Santiago, Chile
2Facultad de Ingeniería y Ciencias, Universidad Diego Portales, Ejército 441, Santiago, Chile
3Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Monseñor Álvaro del Portillo 12455, Las Condes, Santiago, Chile

Correspondence should be addressed to Sebasti√°n Maldonado; lc.sednau@odanodlams

Received 4 April 2017; Accepted 24 September 2017; Published 11 January 2018

Academic Editor: Guido Rubboli

Copyright © 2018 Paul Bosch et al. 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.


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.