TY - GEN
T1 - One class support vector machine based filter for improving the classification accuracy of SSVEP BCI
AU - Sun, Gaopeng
AU - Liu, Hui
AU - Shi, Yanhua
AU - Leng, Yue
AU - Lin, Pan
AU - Wang, Ruimin
AU - Yang, Yuankui
AU - Gao, Junfeng
AU - Wang, Haixian
AU - Iramina, Keiji
AU - Ge, Sheng
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the National Basic Research Program of China (2015CB351704), the National Nature Science Foundation of China (61473221, 81271659, and 31500881), the Natural Science Foundation of Jiangsu Province of China under Grant (BK20140621), and the Fundamental Research Funds for the Southeast University.
PY - 2018/2/22
Y1 - 2018/2/22
N2 - 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.
AB - 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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U2 - 10.1109/CISP-BMEI.2017.8302171
DO - 10.1109/CISP-BMEI.2017.8302171
M3 - Conference contribution
AN - SCOPUS:85047569201
T3 - Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
SP - 1
EP - 5
BT - Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
A2 - Qiu, Song
A2 - Liu, Hongying
A2 - Sun, Li
A2 - Wang, Lipo
A2 - Li, Qingli
A2 - Zhou, Mei
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
Y2 - 14 October 2017 through 16 October 2017
ER -