International Conference on Brain Informatics and Health

BIH 2015: Brain Informatics and Health pp 232-241 | Cite as

Classification Accuracy Improvement of Chromatic and High–Frequency Code–Modulated Visual Evoked Potential–Based BCI

  • Daiki Aminaka
  • Shoji Makino
  • Tomasz M. Rutkowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9250)

Abstract

We present results of a classification improvement approach for a code–modulated visual evoked potential (cVEP) based brain–computer interface (BCI) paradigm using four high–frequency flashing stimuli. Previously published research reports presented successful BCI applications of canonical correlation analysis (CCA) to steady–state visual evoked potential (SSVEP) BCIs. Our team already previously proposed the combined CCA and cVEP techniques’ BCI paradigm. The currently reported study presents the further enhanced results using a support vector machine (SVM) method in application to the cVEP–based BCI.

Keywords

Brain–computer interfaces ERP cVEP EEG classification 

References

  1. 1.
    Aminaka, D., Makino, S., Rutkowski, T.M.: Chromatic and high-requency cVEP-based BCI paradigm. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society, August 25–29, 2015. (accepted, in press)Google Scholar
  2. 2.
    Bakardjian, H., Tanaka, T., Cichocki, A.: Optimization of SSVEP brain responses with application to eight-command brain-computer interface. Neuroscience Letters 469(1), 34–38 (2010)CrossRefGoogle Scholar
  3. 3.
    Bin, G., Gao, X., Wang, Y., Hong, B., Gao, S.: VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier]. IEEE Computational Intelligence Magazine 4(4), 22–26 (2009)CrossRefGoogle Scholar
  4. 4.
    Bin, G., Gao, X., Wang, Y., Li, Y., Hong, B., Gao, S.: A high-speed BCI based on code modulation VEP. Journal of Neural Engineering 8(2), 025015 (2011)CrossRefGoogle Scholar
  5. 5.
    Bin, G., Gao, X., Yan, Z., Hong, B., Gao, S.: An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. Journal of Neural Engineering 6(4), 046002 (2009)CrossRefGoogle Scholar
  6. 6.
    Plum, F., Posner, J.B.: The Diagnosis of Stupor and Coma. FA Davis, Philadelphia (1966)Google Scholar
  7. 7.
    Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., Bertrand, O., Lécuyer, A.: Openvibe: an open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments. Presence: Teleoperators and Virtual Environments 19(1), 35–53 (2010)CrossRefGoogle Scholar
  8. 8.
    Sakurada, T., Kawase, T., Komatsu, T., Kansaku, K.: Use of high-frequency visual stimuli above the critical flicker frequency in a SSVEP-based BMI. Clinical Neurophysiology (2014). (online first)Google Scholar
  9. 9.
    Wolpaw, J., Wolpaw, E.W. (eds.): Brain-Computer Interfaces: Principles and Practice. Oxford University Press (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Daiki Aminaka
    • 1
  • Shoji Makino
    • 1
  • Tomasz M. Rutkowski
    • 1
  1. 1.Life Science Center of TARA at University of TsukubaTsukuba, IbarakiJapan

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