title: Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method.
abstract: Based on the neural efficiency hypothesis and task-induced EEG gamma-band response (GBR), this study investigated the brain regions where neural resource could be most efficiently recruited by the math-gifted adolescents in response to varying cognitive demands. In this experiment, various GBR-based mental states were generated with three factors (level of mathematical ability, task complexity, and short-term learning) modulating the level of neural activation. A feature subset selection method based on the sequential forward floating search algorithm was used to identify an "optimal" combination of EEG channel locations, where the corresponding GBR feature subset could obtain the highest accuracy in discriminating pairwise mental states influenced by each experiment factor. The integrative results from multi-factor selections suggest that the right-lateral fronto-parietal system is highly involved in neural efficiency of the math-gifted brain, primarily including the bilateral superior frontal, right inferior frontal, right-lateral central and right temporal regions. By means of the localization method based on single-trial classification of mental states, new GBR features and EEG channel-based brain regions related to mathematical giftedness were identified, which could be useful for the brain function improvement of children/adolescents in mathematical learning through brain-computer interface systems.
authors.affiliation: Key Lab of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, 210096 Jiangsu China.
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Key Lab of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, 210096 Jiangsu China ; School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.
authors.forename: John Q
authors.initials: JQ
authors.lastname: Gan
authors.affiliation: Key Lab of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, 210096 Jiangsu China.
authors.forename: Haixian
authors.initials: H
authors.lastname: Wang
keywords: EEG Gamma-band response; Feature subset selection; Math-gifted adolescents; Neural efficiency; Numerical inductive reasoning