Classification of EEG in a steady state visual evoked potential based brain computer interface experiment

İŞCAN Z., Özkaya Ö., Ölmez Z.

10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2011, Ljubljana, Slovenia, 14 - 16 April 2011, vol.6594 LNCS, pp.81-88 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 6594 LNCS
  • Doi Number: 10.1007/978-3-642-20267-4_9
  • City: Ljubljana
  • Country: Slovenia
  • Page Numbers: pp.81-88
  • Keywords: BCI, Classification, EEG, SSVEP
  • Istanbul Medipol University Affiliated: No

Abstract

In this paper, electroencephalogram (EEG) signals of 20 subjects are classified in a steady state visual evoked potential (SSVEP) based brain computer interface (BCI) system by using 4 different stimulation frequencies in a program created by Visual C#. After applying proper pre-processing methods, power spectral density (PSD) based features are extracted around first and second harmonics of the stimulation frequencies. Average classification performance obtained from 20 subjects in 4-class classification is 83.62% with Nearest Mean Classifier (NMC). Results for 5-class classification, EEG segment size and gender differences are also analyzed in a detailed manner. The classification method is simple and very suitable for real-time experiments. © 2011 Springer-Verlag.