One class support vector machine based filter for improving the classification accuracy of SSVEP BCI

Gaopeng Sun, Hui Liu, Yanhua Shi, Yue Leng, Pan Lin, Ruimin Wang, Yuankui Yang, Junfeng Gao, Haixian Wang, Keiji Iramina, Sheng Ge

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

Abstract

Canonical correlation analysis (CCA) has been proved to be effective in the detection of steady state visual evoked potential (SSVEP) signals. However, the CCA method only chooses the frequency in the reference mode that corresponds to the maximum correlation value as the target. This may make the CCA output less robust. In this study, we propose a one-class support vector machine based filter to filter the sequences of correlation values in the process of the detection of SSVEP signals. The results demonstrate that the classification accuracy improved over different time windows for all subjects and the improvement achieved approximately 10% for some subjects. Moreover, the ratio of instructions that were filtered incorrectly was relative low (less than 5%) if the SSVEP signals were generated effectively.

Original languageEnglish
Title of host publicationProceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
EditorsSong Qiu, Hongying Liu, Li Sun, Lipo Wang, Qingli Li, Mei Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538619377
DOIs
Publication statusPublished - Feb 22 2018
Event10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 - Shanghai, China
Duration: Oct 14 2017Oct 16 2017

Publication series

NameProceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
Volume2018-January

Other

Other10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
CountryChina
CityShanghai
Period10/14/1710/16/17

Fingerprint

Visual Evoked Potentials
Bioelectric potentials
Support vector machines
Support Vector Machine

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering

Cite this

Sun, G., Liu, H., Shi, Y., Leng, Y., Lin, P., Wang, R., ... Ge, S. (2018). One class support vector machine based filter for improving the classification accuracy of SSVEP BCI. In S. Qiu, H. Liu, L. Sun, L. Wang, Q. Li, & M. Zhou (Eds.), Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 (pp. 1-5). (Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISP-BMEI.2017.8302171

One class support vector machine based filter for improving the classification accuracy of SSVEP BCI. / Sun, Gaopeng; Liu, Hui; Shi, Yanhua; Leng, Yue; Lin, Pan; Wang, Ruimin; Yang, Yuankui; Gao, Junfeng; Wang, Haixian; Iramina, Keiji; Ge, Sheng.

Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017. ed. / Song Qiu; Hongying Liu; Li Sun; Lipo Wang; Qingli Li; Mei Zhou. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-5 (Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017; Vol. 2018-January).

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

Sun, G, Liu, H, Shi, Y, Leng, Y, Lin, P, Wang, R, Yang, Y, Gao, J, Wang, H, Iramina, K & Ge, S 2018, One class support vector machine based filter for improving the classification accuracy of SSVEP BCI. in S Qiu, H Liu, L Sun, L Wang, Q Li & M Zhou (eds), Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017. Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017, Shanghai, China, 10/14/17. https://doi.org/10.1109/CISP-BMEI.2017.8302171
Sun G, Liu H, Shi Y, Leng Y, Lin P, Wang R et al. One class support vector machine based filter for improving the classification accuracy of SSVEP BCI. In Qiu S, Liu H, Sun L, Wang L, Li Q, Zhou M, editors, Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-5. (Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017). https://doi.org/10.1109/CISP-BMEI.2017.8302171
Sun, Gaopeng ; Liu, Hui ; Shi, Yanhua ; Leng, Yue ; Lin, Pan ; Wang, Ruimin ; Yang, Yuankui ; Gao, Junfeng ; Wang, Haixian ; Iramina, Keiji ; Ge, Sheng. / One class support vector machine based filter for improving the classification accuracy of SSVEP BCI. Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017. editor / Song Qiu ; Hongying Liu ; Li Sun ; Lipo Wang ; Qingli Li ; Mei Zhou. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-5 (Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017).
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title = "One class support vector machine based filter for improving the classification accuracy of SSVEP BCI",
abstract = "Canonical correlation analysis (CCA) has been proved to be effective in the detection of steady state visual evoked potential (SSVEP) signals. However, the CCA method only chooses the frequency in the reference mode that corresponds to the maximum correlation value as the target. This may make the CCA output less robust. In this study, we propose a one-class support vector machine based filter to filter the sequences of correlation values in the process of the detection of SSVEP signals. The results demonstrate that the classification accuracy improved over different time windows for all subjects and the improvement achieved approximately 10{\%} for some subjects. Moreover, the ratio of instructions that were filtered incorrectly was relative low (less than 5{\%}) if the SSVEP signals were generated effectively.",
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AU - Shi, Yanhua

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AU - Wang, Ruimin

AU - Yang, Yuankui

AU - Gao, Junfeng

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