We present results of a classification improvement approach for a code–modulated visual evoked potential (cVEP) based brain– computer interface (BCI) paradigm using four high–frequency flashing stimuli. Previously published research reports presented successful BCI applications of canonical correlation analysis (CCA) to steady–state visual evoked potential (SSVEP) BCIs. Our team already previously proposed the combined CCA and cVEP techniques’ BCI paradigm. The currently reported study presents the further enhanced results using a support vector machine (SVM) method in application to the cVEP–based BCI.
CITATION STYLE
Aminaka, D., Makino, S., & Rutkowski, T. M. (2015). Classification accuracy improvement of chromatic and high–frequency code–modulated visual evoked potential–based BCI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9250, pp. 232–241). Springer Verlag. https://doi.org/10.1007/978-3-319-23344-4_23
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