SVM classification study of code-modulated visual evoked potentials

Daiki Aminaka, Shoji Makino, Tomasz M. Rutkowski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1065-1070
Number of pages6
ISBN (Electronic)9789881476807
DOIs
Publication statusPublished - 2016 Feb 19
Externally publishedYes
Event2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong
Duration: 2015 Dec 162015 Dec 19

Publication series

Name2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015

Other

Other2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
CountryHong Kong
CityHong Kong
Period15/12/1615/12/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Modelling and Simulation
  • Signal Processing

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