@inproceedings{9aba7cd58c974b3596a5a0da5ff06c99,
title = "Classification of EEG in a steady state visual evoked potential based brain computer interface experiment",
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.",
keywords = "BCI, Classification, EEG, SSVEP",
author = "Zafer I{\c s}can and {\"O}zen {\"O}zkaya and Z{\"u}mray Dokur",
year = "2011",
doi = "10.1007/978-3-642-20267-4\_9",
language = "English",
isbn = "9783642202667",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "81--88",
booktitle = "Adaptive and Natural Computing Algorithms - 10th International Conference, ICANNGA 2011, Proceedings",
edition = "PART 2",
note = "10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2011 ; Conference date: 14-04-2011 Through 16-04-2011",
}