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Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 680769, 7 pages
http://dx.doi.org/10.1155/2015/680769
Research Article

Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems

1Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
2Center for Information in Bio-Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China

Received 10 July 2015; Revised 28 September 2015; Accepted 28 September 2015

Academic Editor: Irena Cosic

Copyright © 2015 Dongrui 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. J. R. Wolpaw and D. J. McFarland, “Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 51, pp. 17849–17854, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Zhou, E. Yin, Y. Liu, J. Jiang, and D. Hu, “A novel task-oriented optimal design for P300-based brain-computer interfaces,” Journal of Neural Engineering, vol. 11, no. 5, Article ID 056003, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Takahashi, K. Takeda, Y. Otaka et al., “Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: a feasibility study,” Journal of NeuroEngineering and Rehabilitation, vol. 9, article 56, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clinical Neurophysiology, vol. 113, no. 6, pp. 767–791, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. C. Guger, H. Ramoser, and G. Pfurtscheller, “Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI),” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 4, pp. 447–456, 2000. View at Publisher · View at Google Scholar · View at Scopus
  6. V. Bostanov, “BCI competition 2003—data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1057–1061, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. F. Guo, B. Hong, X. Gao, and S. Gao, “A brain-computer interface using motion-onset visual evoked potential,” Journal of Neural Engineering, vol. 5, no. 4, pp. 477–485, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neuroscience Letters, vol. 553, pp. 84–89, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neuroscience Letters, vol. 587, pp. 87–92, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Zhang, P. Xu, L. Guo, Y. Zhang, P. Li, and D. Yao, “Z-Score linear discriminant analysis for EEG based brain-computer interfaces,” PLoS ONE, vol. 8, no. 9, Article ID e74433, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. Li, C. Guan, H. Li, and Z. Chin, “A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system,” Pattern Recognition Letters, vol. 29, no. 9, pp. 1285–1294, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” Journal of Neural Engineering, vol. 4, no. 2, pp. R1–R13, 2007. View at Google Scholar
  13. P. Xu, P. Yang, X. Lei, and D. Yao, “An enhanced probabilistic LDA for multi-class brain computer interface,” PLoS ONE, vol. 6, no. 1, Article ID e14634, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. X. Lei, P. Yang, and D. Yao, “An empirical bayesian framework for brain-computer interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 17, no. 6, pp. 521–529, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, NY, USA, 2006.
  16. B. Hong, F. Guo, T. Liu, X. R. Gao, and S. K. Gao, “N200-speller using motion-onset visual response,” Clinical Neurophysiology, vol. 120, no. 9, pp. 1658–1666, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. R. Zhang, P. Xu, R. Chen et al., “An adaptive motion-onset VEP-based brain-computer interface,” IEEE Transactions on Autonomous Mental Development, 2015. View at Publisher · View at Google Scholar
  18. M. Kuba, Z. Kubová, J. Kremláček, and J. Langrová, “Motion-onset VEPs: characteristics, methods, and diagnostic use,” Vision Research, vol. 47, no. 2, pp. 189–202, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Experimental Brain Research, vol. 232, no. 2, pp. 555–564, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. N. Naseer and K. Hong, “fNIRS-based brain-computer interfaces: a review,” Frontiers in Human Neuroscience, vol. 9, article 3, 2015. View at Publisher · View at Google Scholar