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

  • Authors:

  • Zafer İşcan;Özen Özkaya;Zümray Dokur

  • Affiliations:

  • Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey;Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey;Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey

  • Venue:
  • ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
  • Year:
  • 2011

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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.