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

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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.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 31130025, the National Basic Research Program of China under Grant 2015CB351704, the National Natural Science Foundation of China under Grant 61375118, and the Program for New Century Excellent Talents in Universities of China under Grant NCET-12-0115. The authors would like to thank the anonymous reviewers and editors for their thoughtful comments and suggestions.

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Authors and Affiliations

  1. Key Lab of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, 210096, Jiangsu, China

    Li Zhang, John Q. Gan & Haixian Wang

  2. School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK

    John Q. Gan

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  1. Li Zhang
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  2. John Q. Gan
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  3. Haixian Wang
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Correspondence to Haixian Wang.

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Zhang, L., Gan, J.Q. & Wang, H. Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method. Cogn Neurodyn 9, 495–508 (2015). https://doi.org/10.1007/s11571-015-9345-1

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  • DOI: https://doi.org/10.1007/s11571-015-9345-1

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