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Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method

Zhang, Li and Gan, John Q and Wang, Haixian (2015) 'Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method.' Cognitive Neurodynamics, 9 (5). pp. 495-508. ISSN 1871-4080

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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.

Item Type: Article
Uncontrolled Keywords: Neural efficiency; Math-gifted adolescents; Numerical inductive reasoning; EEG Gamma-band response; Feature subset selection
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 09 Oct 2015 10:00
Last Modified: 15 Jan 2022 00:23

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