Table of Contents Author Guidelines Submit a Manuscript
Table of Contents Alerts

To receive news and publication updates for Computational Intelligence and Neuroscience, enter your email address in the box below.

Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 6184823, 10 pages
http://dx.doi.org/10.1155/2016/6184823
Research Article

Explore Interregional EEG Correlations Changed by Sport Training Using Feature Selection

Jia Gao,1 Wei Wang,1 and Ji Zhang2

1Laboratory of Machine Learning and Cognition, Nanjing Normal University, Nanjing 210097, China
2Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia

Received 7 June 2015; Revised 5 December 2015; Accepted 8 December 2015

Academic Editor: Jens Christian Claussen

Copyright © 2016 Jia Gao 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.

Linked References

  1. D. B. Willingham, “A neuropsychological theory of motor skill learning,” Psychological Review, vol. 105, no. 3, pp. 558–584, 1998. View at Publisher · View at Google Scholar · View at Scopus
  2. G. Wei and J. Luo, “Sport expert's motor imagery: functional imaging of professional motor skills and simple motor skills,” Brain Research, vol. 1341, pp. 52–62, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. D. E. Callan and E. Naito, “Neural processes distinguishing elite from expert and novice athletes,” Cognitive and Behavioral Neurology, vol. 27, no. 4, pp. 183–188, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. S. W. H. Wong, R. H. M. Chan, and J. N. Mak, “Spectral modulation of frontal EEG during motor skill acquisition: a mobile EEG study,” International Journal of Psychophysiology, vol. 91, no. 1, pp. 16–21, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. N. Howard and H. Lieberman, “BrainSpace: relating neuroscience to knowledge about everyday life,” Cognitive Computation, vol. 6, no. 1, pp. 35–44, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. U. Halsband and R. K. Lange, “Motor learning in man: a review of functional and clinical studies,” Journal of Physiology Paris, vol. 99, no. 4–6, pp. 414–424, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Cousijn, K. Zanolie, R. J. M. Munsters, S. W. Kleibeuker, and E. A. Crone, “The relation between resting state connectivity and creativity in adolescents before and after training,” PLoS ONE, vol. 9, no. 9, Article ID e105780, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. M. W. Voss, R. S. Prakash, K. I. Erickson et al., “Effects of training strategies implemented in a complex videogame on functional connectivity of attentional networks,” NeuroImage, vol. 59, no. 1, pp. 138–148, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. M. W. Voss, K. I. Erickson, R. S. Prakash et al., “Functional connectivity: a source of variance in the association between cardiorespiratory fitness and cognition?” Neuropsychologia, vol. 48, no. 5, pp. 1394–1406, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Millett, “Hans Berger: from psychic energy to the EEG,” Perspectives in Biology and Medicine, vol. 44, no. 4, pp. 522–542, 2001. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Fan and W. Wang, “Detection of relevance between long-term different professional training and brain development using EEG,” Advanced Materials Research, vol. 179-180, pp. 886–890, 2011. View at Google Scholar
  12. G. Deco, V. K. Jirsa, and A. R. McIntosh, “Emerging concepts for the dynamical organization of resting-state activity in the brain,” Nature Reviews Neuroscience, vol. 12, no. 1, pp. 43–56, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Murias, S. J. Webb, J. Greenson, and G. Dawson, “Resting state cortical connectivity reflected in EEG coherence in individuals with autism,” Biological Psychiatry, vol. 62, no. 3, pp. 270–273, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Wu, R. Srinivasan, A. Kaur, and S. C. Cramer, “Resting-state cortical connectivity predicts motor skill acquisition,” NeuroImage, vol. 91, pp. 84–90, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. D. M. Herz, M. S. Christensen, C. Reck et al., “Task-specific modulation of effective connectivity during two simple unimanual motor tasks: a 122-channel EEG study,” NeuroImage, vol. 59, no. 4, pp. 3187–3193, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. E. Bullmore and O. Sporns, “Complex brain networks: graph theoretical analysis of structural and functional systems,” Nature Reviews Neuroscience, vol. 10, no. 3, pp. 186–198, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Krishnan, L. J. Williams, A. R. McIntosh, and H. Abdi, “Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review,” NeuroImage, vol. 56, no. 2, pp. 455–475, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. E. Gerardin, A. Sirigu, S. Léhericy et al., “Partially overlapping neural networks for real and imagined hand movements,” Cerebral Cortex, vol. 10, no. 11, pp. 1093–1104, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Jeannerod, “The representing brain: neural correlates of motor intention and imagery,” Behavioral and Brain Sciences, vol. 17, no. 2, pp. 187–245, 1994. View at Publisher · View at Google Scholar · View at Scopus
  20. M. F. Lafleur, P. L. Jackson, F. Malouin, C. L. Richards, A. C. Evans, and J. Doyon, “Motor learning produces parallel dynamic functional changes during the execution and imagination of sequential foot movements,” NeuroImage, vol. 16, no. 1, pp. 142–157, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. V. Kaiser, G. Bauernfeind, A. Kreilinger et al., “Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG,” NeuroImage, vol. 85, pp. 432–444, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. U. Debarnot, M. Sperduti, F. Di Rienzo, and A. Guillot, “Experts bodies, experts minds: how physical and mental training shape the brain,” Frontiers in Human Neuroscience, vol. 8, article 280, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Subasi, “EEG signal classification using wavelet feature extraction and a mixture of expert model,” Expert Systems with Applications, vol. 32, no. 4, pp. 1084–1093, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, “Using EEG spectral components to assess algorithms for detecting fatigue,” Expert Systems with Applications, vol. 36, no. 2, pp. 2352–2359, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. F. Galton, “Typical Laws of Heredity,” Nature, vol. 15, no. 389, pp. 512–514, 1877. View at Publisher · View at Google Scholar
  26. J. Hauke and T. Kossowski, “Comparison of values of pearson's and spearman's correlation coefficients on the same sets of data,” Quaestiones Geographicae, vol. 30, no. 2, pp. 87–93, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. R. Rosipal and N. Krämer, “Overview and recent advances in partial least squares,” in Subspace, Latent Structure and Feature Selection, vol. 3940 of Lecture Notes in Computer Science, pp. 34–51, Springer, Berlin, Germany, 2006. View at Publisher · View at Google Scholar
  28. S. Wold, L. Eriksson, J. Trygg et al., The PLS Method—Partial Least Squares Projections to Latent Structures-and Its Applications in Industrial RDP (Research, Development, and Production), Umeå University, Umeå, Sweden, 2004.
  29. A. Korik, N. Siddique, R. Sosnik et al., “Correlation of EEG band power and hand motion trajectory,” in Proceedings of the 6th International Brain-Computer Interface Conference, Graz, Austria, September 2014.
  30. B. C. M. van Wijk, V. Litvak, K. J. Friston, and A. Daffertshofer, “Nonlinear coupling between occipital and motor cortex during motor imagery: a dynamic causal modeling study,” NeuroImage, vol. 71, pp. 104–113, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Sami and R. C. Miall, “Graph network analysis of immediate motor-learning induced changes in resting state BOLD,” Frontiers in Human Neuroscience, vol. 7, article 166, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. M. J. Farah, F. Péronnet, M. A. Gonon, and M. H. Giard, “Electrophysiological evidence for a shared representational medium for visual images and visual percepts,” Journal of Experimental Psychology: General, vol. 117, no. 3, pp. 248–257, 1988. View at Publisher · View at Google Scholar · View at Scopus
  33. M. Petrides, “Frontal lobes and behaviour,” Current Opinion in Neurobiology, vol. 4, no. 2, pp. 207–211, 1994. View at Publisher · View at Google Scholar · View at Scopus
  34. J. D. Power, D. A. Fair, B. L. Schlaggar, and S. E. Petersen, “The development of human functional brain networks,” Neuron, vol. 67, no. 5, pp. 735–748, 2010. View at Publisher · View at Google Scholar · View at Scopus
  35. Y. Chang, J.-J. Lee, J.-H. Seo et al., “Neural correlates of motor imagery for elite archers,” NMR in Biomedicine, vol. 24, no. 4, pp. 366–372, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. M. J. Farah, L. L. Weisberg, M. Monheit, and F. Peronnet, “Brain activity underlying mental imagery: event-related potentials during mental image generation,” Journal of Cognitive Neuroscience, vol. 1, no. 4, pp. 302–316, 1989. View at Publisher · View at Google Scholar · View at Scopus
  37. S. Salenius and R. Hari, “Synchronous cortical oscillatory activity during motor action,” Current Opinion in Neurobiology, vol. 13, no. 6, pp. 678–684, 2003. View at Publisher · View at Google Scholar · View at Scopus
  38. S. P. Deeny, A. J. Haufler, M. Saffer, and B. D. Hatfield, “Electroencephalographic coherence during visuomotor performance: a comparison of cortico-cortical communication in experts and novices,” Journal of Motor Behavior, vol. 41, no. 2, pp. 106–116, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. J.-M. Schoffelen, R. Oostenveld, and P. Fries, “Imaging the human motor system's β-band synchronization during isometric contraction,” NeuroImage, vol. 41, no. 2, pp. 437–447, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. A. K. Engel and P. Fries, “β-band oscillations—signalling the status quo?” Current Opinion in Neurobiology, vol. 20, no. 2, pp. 156–165, 2010. View at Publisher · View at Google Scholar · View at Scopus