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All issues Volume 12 (2017) ITM Web Conf., 12 (2017) 01001 References
Open Access
Issue
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
Article Number 01001
Number of page(s) 4
Section Session 1: Robotics
DOI https://doi.org/10.1051/itmconf/20171201001
Published online 05 September 2017
  1. J. R. Wolpaw and D. J. McFarland, “Brain-computer interfaces for communication and control,” Clinical Neurophysiology, vol. 113, pp. 767–791, 2002. [Google Scholar]
  2. E. C. Leuthardt, G. Schalk, J. R. Wolpaw, and J. G. Ojemann, “A brain-computer interface using electrocorticographic signals in humans,” Journal of Neural Engineeringy, vol. 1, pp. 63–71, 2004. [CrossRef] [Google Scholar]
  3. S. Sutton, M. Braren, J. Zubin, and E. R. John, “Evoked-potential correlates of stimulus uncertainty,” Science, vol. 150, pp. 1187–1188, 1965. [CrossRef] [Google Scholar]
  4. J. Polich, “On the relationship between EEG and P300: Individual differences, aging, and ultradian rhythms,” International Journal of Psychophysiology, vol. 26, pp. 299–317, 1997. [CrossRef] [Google Scholar]
  5. J. D. Bayliss and D. H. Ballard, “Single trial P3 epoch recognition in a virtual environment,” Neurocomputing, vol. 32, pp. 637–642, 2000. [CrossRef] [Google Scholar]
  6. A. R. Miller, J. P. Rosenfeld, M. Soskins, and M. Jhee, “P300 Amplitude and topography in an autobiographical oddball paradigm involving simulated amnesia,” International Journal of Psychophysiology, vol. 16, pp. 1–11, 2002. [CrossRef] [Google Scholar]
  7. J. R. Wolpaw, D. J. McFarland, G. W. Neat, and C. A. Forneris, “An EEG-based brain-computer interface for cursor control,” Electroencephalography and clinical neurophysiology, vol. 78, pp. 252–259, 1991. [CrossRef] [Google Scholar]
  8. R. T. Lauer, P. H. Peckham, K. L. Kilgore, and W. J. Heetderks, “Applications of cortical signals to neuroprosthetic control: A critical review,” IEEE Transactions on Rehabilitation Engineering, vol. 8, pp. 205–208, 2000. [CrossRef] [Google Scholar]
  9. B. Z. Allison and J. A. Pineda, “ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, pp. 110–113, 2003. [CrossRef] [Google Scholar]
  10. E. W. Sellers and D. J. McFarland, “Toward enhanced P300 speller performance,” Journal of Neuroscience Methods, vol. 167, pp. 15–21, 2008. [CrossRef] [Google Scholar]
  11. N. Xu, X. Gao, and B. Hong, “BCI Competition 2003-Data set IIb: enhancing P300 wave detection using ICA based subspace projections,” IEEE Transactions on Bio-medical Engineering, vol. 51, pp. 1067–1072, 2004. [CrossRef] [Google Scholar]
  12. R. Fazel-Rezai and K. Abhari, “A region-based P300 speller for brain-computer interface,” Canadian Journal of Electrical and Computer Engineering, vol. 34, pp. 81–85, 2009. [CrossRef] [Google Scholar]
  13. L. A. Farwell and E. Donchin, “Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials,” Electroencephalography and clinical neurophysiology, vol. 70, pp. 510–523, 1988. [Google Scholar]
  14. B. Wu, Y. Su, J. Zhang, and X. Li, “A Virtual Chinese Keyboard BCI System Based on P300 Potentials,” Acta Electronica Sinica, vol. 37, pp. 1733–1738, 2009. [Google Scholar]
  15. J. Jin, B. Z. Allison, C. Brunner, and B. Wang, “P300 Chinese input system based on Bayesian LDA,” Biomedizinische Technik Biomedical Engineering, vol. 55, pp. 5–18, 2010. [CrossRef] [Google Scholar]
  16. J. Minett, G. Peng, L. Zhou, H. Y. Zheng, and S. Wang, “An Assistive Communication Brain-Computer Interface for Chinese Text Input,” 4th International Conference on Bioinformatics and Biomedical Engineering, Jun. 2010. [Google Scholar]
  17. K. T. Sun, T. W. Huang, and M. C. Chen, “Design of Chinese Spelling System Based on ERP,” 11th International Conference on Bioinformatics and Bioengineering, vol. 1997, pp. 310–313, Jun. 2010. [Google Scholar]
  18. X. Xu and H. J. Fang, “A P300-based BCI system for Online Chinese input,” Journal of Huaqiao University (Natural Science), vol. 36, pp. 269–274, 2015. [Google Scholar]
  19. T. W. Huang, Y. H. Tai, Y. J. Tian, and K. T. Sun, “The fastest BCI for writing Chinese characters using brain waves,” 4th Global Congress on Intelligent Systems, pp. 346–349, 2013. [Google Scholar]
  20. T. Kaufmann, S. M. Schulz, C. Grünzinger, and A. Kübler, “Flashing characters with famous faces improves ERP-based brain-computer interface performance,” Journal of Neural Engineering, vol. 8, pp. 173–178, 2011. [CrossRef] [Google Scholar]
  21. Y. Zhang, Q. Zhao, J. JIn, and X. Wang, “A novel BCI based on ERP components sensitive to configural processing of human faces,” Journal of Neural Engineering, vol. 9, pp. 26018–26030, 2012. [CrossRef] [Google Scholar]
  22. D. B. Ryan, G. E. Frye, G. Townsend, D. R. Berry, and S. Mesag, “Predictive spelling with a P300-based brain-computer interface: Increasing the rate of communication,” International journal of human-computer interaction, vol. 27, pp. 69–84, 2011. [CrossRef] [Google Scholar]

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