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Proceedings Paper

Sinusoid-assisted MEMD-based CCA method for SSVEP-based BCI improvement
Author(s): Gaopeng Sun; Yanhua Shi; Hui Liu; Yichuan Jiang; Pan Lin; Junfeng Gao; Ruimin Wang; Yue Leng; Yuankui Yang; Iramina Keiji; Sheng Ge
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Paper Abstract

Although the canonical correlation analysis (CCA) algorithm has been applied successfully to steady-state visual evoked potential (SSVEP) detection, artifacts and unrelated brain activities may affect the performance of SSVEP-based brain– computer interface systems. Extracting the characteristic frequency sub-bands is an effective method of enhancing the signal-to-noise-ratio of SSVEP signals. The sinusoid-assisted multivariate extension of empirical mode decomposition (SA-MEMD) algorithm is a powerful method of spectral decomposition. In this study, we propose an SA-MEMD-based CCA method for SSVEP detection. Experimental results suggest that the SA-MEMD-based CCA algorithm is a useful method for the detection of typical SSVEP signals. The SA-MEMD-based CCA algorithm reached a classification accuracy of 88.3% for a window of 4 s and outperformed the standard CCA algorithm by 2.8%.

Paper Details

Date Published: 29 October 2018
PDF: 9 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083628 (29 October 2018); doi: 10.1117/12.2514420
Show Author Affiliations
Gaopeng Sun, Southeast Univ. (China)
Yanhua Shi, Huanghe Science and Technology College (China)
Hui Liu, Southeast Univ. (China)
Yichuan Jiang, Southeast Univ. (China)
Pan Lin, South-Central Univ. for Nationalities (China)
Junfeng Gao, South-Central Univ. for Nationalities (China)
Ruimin Wang, Kyushu Univ. (Japan)
Yue Leng, Southeast Univ. (China)
Yuankui Yang, Southeast Univ. (China)
Iramina Keiji, Kyushu Univ. (Japan)
Sheng Ge, Southeast Univ. (China)

Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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