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.
CITATION STYLE
Işcan, Z., Özkaya, Ö., & Dokur, Z. (2011). Classification of EEG in a steady state visual evoked potential based brain computer interface experiment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6594 LNCS, pp. 81–88). https://doi.org/10.1007/978-3-642-20267-4_9
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