EEG Filtering Optimization for Code–Modulated Chromatic Visual Evoked Potential–Based Brain–Computer Interface

  • Daiki Aminaka
  • Shoji Makino
  • Tomasz M. Rutkowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9359)


We present visual BCI classification accuracy improved results after application of high– and low–pass filters to an electroencephalogram (EEG) containing code–modulated visual evoked potentials (cVEPs). The cVEP responses are applied for the brain–computer interface (BCI) in four commands paradigm mode. The purpose of this project is to enhance BCI accuracy using only the single trial cVEP response. We also aim at identification of the most discriminable EEG bands suitable for the broadband visual stimuli. We report results from a pilot study optimizing the EEG filtering using infinite impulse response filters in application to feature extraction for a linear support vector machine (SVM) classification method. The goal of the presented study is to develop a faster and more reliable BCI to further enhance the symbiotic relationships between humans and computers.


Brain–computer interface ERP EEG classification cVEP 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aminaka, D., Makino, S., Rutkowski, T.M.: Chromatic and high-frequency 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 (2015). (accepted, in press)Google Scholar
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Plum, F., Posner, J.B.: The Diagnosis of Stupor and Coma. FA Davis, Philadelphia (1966)Google Scholar
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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 TsukubaTsukubaJapan

Personalised recommendations