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, Iramina Keiji, 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

All Science Journal Classification (ASJC) codes

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

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