SVM classification study of code-modulated visual evoked potentials

Daiki Aminaka; Shoji Makino; Tomasz M. Rutkowski

doi:10.1109/APSIPA.2015.7415435

SVM classification study of code-modulated visual evoked potentials

Daiki Aminaka, Shoji Makino, Tomasz M. Rutkowski

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1065-1070
Number of pages	6
ISBN (Electronic)	9789881476807
DOIs	https://doi.org/10.1109/APSIPA.2015.7415435
Publication status	Published - 2016 Feb 19
Externally published	Yes
Event	2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong Duration: 2015 Dec 16 → 2015 Dec 19

Publication series

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

Other

Other	2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
Country	Hong Kong
City	Hong Kong
Period	15/12/16 → 15/12/19

ASJC Scopus subject areas

Artificial Intelligence
Modelling and Simulation
Signal Processing

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Aminaka, D., Makino, S., & Rutkowski, T. M. (2016). SVM classification study of code-modulated visual evoked potentials. In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 (pp. 1065-1070). [7415435] (2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2015.7415435

SVM classification study of code-modulated visual evoked potentials. / Aminaka, Daiki; Makino, Shoji; Rutkowski, Tomasz M.

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1065-1070 7415435 (2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Aminaka, D, Makino, S & Rutkowski, TM 2016, SVM classification study of code-modulated visual evoked potentials. in 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015., 7415435, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1065-1070, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015, Hong Kong, Hong Kong, 15/12/16. https://doi.org/10.1109/APSIPA.2015.7415435

Aminaka D, Makino S, Rutkowski TM. SVM classification study of code-modulated visual evoked potentials. In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1065-1070. 7415435. (2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015). https://doi.org/10.1109/APSIPA.2015.7415435

Aminaka, Daiki ; Makino, Shoji ; Rutkowski, Tomasz M. / SVM classification study of code-modulated visual evoked potentials. 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1065-1070 (2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015).

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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.",

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SVM classification study of code-modulated visual evoked potentials

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