DocumentCode :
3752185
Title :
SVM classification study of code-modulated visual evoked potentials
Author :
Daiki Aminaka;Shoji Makino;Tomasz M. Rutkowski
Author_Institution :
Department of Computer Science and Life Science Center of TARA, University of Tsukuba, Tsukuba, Ibaraki, Japan
fYear :
2015
Firstpage :
1065
Lastpage :
1070
Abstract :
We present a study of a support vector machine (SVM) application to brain-computer interface (BCI) paradigm. Four SVM kernel functions are evaluated in order to maximize classification accuracy of a four classes-based BCI paradigm utilizing a code-modulated visual evoked potential (cVEP) response within the captured EEG signals. Our previously published reports applied only the linear SVM, which already outperformed a more classical technique of a canonical correlation analysis (CCA). In the current study we additionally test and compare classification accuracies of polynomial, radial basis and sigmoid kernels, together with the classical linear (non-kernel-based) SVMs in application to the cVEP BCI.
Keywords :
"Support vector machines","Electroencephalography","Kernel","Light emitting diodes","Visualization","Standards","Training"
Publisher :
ieee
Conference_Titel :
Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2015 Asia-Pacific
Type :
conf
DOI :
10.1109/APSIPA.2015.7415435
Filename :
7415435
Link To Document :
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