Sinusoid-assisted MEMD-based CCA method for SSVEP-based BCI improvement

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

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

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%.

Original languageEnglish
Title of host publication2018 International Conference on Image and Video Processing, and Artificial Intelligence
EditorsRuidan Su
PublisherSPIE
ISBN (Electronic)9781510623101
DOIs
Publication statusPublished - Jan 1 2018
Event2018 International Conference on Image and Video Processing, and Artificial Intelligence, IVPAI 2018 - Shanghai, China
Duration: Aug 15 2018Aug 17 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10836
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2018 International Conference on Image and Video Processing, and Artificial Intelligence, IVPAI 2018
CountryChina
CityShanghai
Period8/15/188/17/18

Fingerprint

Canonical Correlation Analysis
Bioelectric potentials
sine waves
Decomposition
decomposition
Decompose
brain
Brain computer interface
Spectral Decomposition
Decomposition Algorithm
artifacts
Brain
Signal to noise ratio
signal to noise ratios
Vision
Experimental Results

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Sun, G., Shi, Y., Liu, H., Jiang, Y., Lin, P., Gao, J., ... Ge, S. (2018). Sinusoid-assisted MEMD-based CCA method for SSVEP-based BCI improvement. In R. Su (Ed.), 2018 International Conference on Image and Video Processing, and Artificial Intelligence [1083628] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10836). SPIE. https://doi.org/10.1117/12.2514420

Sinusoid-assisted MEMD-based CCA method for SSVEP-based BCI improvement. / Sun, Gaopeng; Shi, Yanhua; Liu, Hui; Jiang, Yichuan; Lin, Pan; Gao, Junfeng; Wang, Ruimin; Leng, Yue; Yang, Yuankui; Iramina, Keiji; Ge, Sheng.

2018 International Conference on Image and Video Processing, and Artificial Intelligence. ed. / Ruidan Su. SPIE, 2018. 1083628 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10836).

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

Sun, G, Shi, Y, Liu, H, Jiang, Y, Lin, P, Gao, J, Wang, R, Leng, Y, Yang, Y, Iramina, K & Ge, S 2018, Sinusoid-assisted MEMD-based CCA method for SSVEP-based BCI improvement. in R Su (ed.), 2018 International Conference on Image and Video Processing, and Artificial Intelligence., 1083628, Proceedings of SPIE - The International Society for Optical Engineering, vol. 10836, SPIE, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, IVPAI 2018, Shanghai, China, 8/15/18. https://doi.org/10.1117/12.2514420
Sun G, Shi Y, Liu H, Jiang Y, Lin P, Gao J et al. Sinusoid-assisted MEMD-based CCA method for SSVEP-based BCI improvement. In Su R, editor, 2018 International Conference on Image and Video Processing, and Artificial Intelligence. SPIE. 2018. 1083628. (Proceedings of SPIE - The International Society for Optical Engineering). https://doi.org/10.1117/12.2514420
Sun, Gaopeng ; Shi, Yanhua ; Liu, Hui ; Jiang, Yichuan ; Lin, Pan ; Gao, Junfeng ; Wang, Ruimin ; Leng, Yue ; Yang, Yuankui ; Iramina, Keiji ; Ge, Sheng. / Sinusoid-assisted MEMD-based CCA method for SSVEP-based BCI improvement. 2018 International Conference on Image and Video Processing, and Artificial Intelligence. editor / Ruidan Su. SPIE, 2018. (Proceedings of SPIE - The International Society for Optical Engineering).
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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{\%}.",
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AU - Gao, Junfeng

AU - Wang, Ruimin

AU - Leng, Yue

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