Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies
Journal
Behavioural Neurology
ISSN
0953-4180
1875-8584
Date Issued
2018
Author(s)
Julio López
Sebastián Maldonado
Type
Resource Types::text::journal::journal article
Abstract
<jats:p>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.</jats:p>
Subjects
support vector machine
;
resting-state fmri
;
phase synchronization
;
feature-selection
;
classification
;
performance
;
mechanisms
;
attention
;
networks
;
patterns