Research on SSVEP feature extraction based on HHT

Considering of high transmission rate and short training time, Steady State Visual Evoked Potential (SSVEP) rapidly becomes a practical signal in Brain-Computer Interface(BCI) system. This paper study the extraction method of SSVEP based on the Hilbert-Huang Transformation. The SSVEP was processed b...

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Published in2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery Vol. 5; pp. 2220 - 2223
Main Authors Li Zhao, Pengxian Yuan, Longteng Xiao, Qingguo Meng, Daofu Hu, Hui Shen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2010
Subjects
Accuracy
BCI
Electric potential
Electroencephalography
Feature extraction
Fisher classifier
Hilbert-Huang Transform
SSVEP
Time frequency analysis
Transforms
Visualization
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Abstract Considering of high transmission rate and short training time, Steady State Visual Evoked Potential (SSVEP) rapidly becomes a practical signal in Brain-Computer Interface(BCI) system. This paper study the extraction method of SSVEP based on the Hilbert-Huang Transformation. The SSVEP was processed by a time-frequency processing system. after empirical mode decomposition and Hilbert-Huang Transform(HHT), an eigenvector detected from the result of HHT was viewed as the characteristics of the SSVEP signal that contains different frequency component. Then the eigenvector is classified in a Fisher classifier. Compared with the (Fast Fourier Transform)FFT, the classification accuracy of a one-minute data can reach more than 85 percent.
AbstractList Considering of high transmission rate and short training time, Steady State Visual Evoked Potential (SSVEP) rapidly becomes a practical signal in Brain-Computer Interface(BCI) system. This paper study the extraction method of SSVEP based on the Hilbert-Huang Transformation. The SSVEP was processed by a time-frequency processing system. after empirical mode decomposition and Hilbert-Huang Transform(HHT), an eigenvector detected from the result of HHT was viewed as the characteristics of the SSVEP signal that contains different frequency component. Then the eigenvector is classified in a Fisher classifier. Compared with the (Fast Fourier Transform)FFT, the classification accuracy of a one-minute data can reach more than 85 percent.
Author Pengxian Yuan
Li Zhao
Qingguo Meng
Daofu Hu
Longteng Xiao
Hui Shen
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  organization: Dept. of Autom. & Electr. Eng., Tianjin Univ. of Technol. & Educ., Tianjin, China
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Snippet Considering of high transmission rate and short training time, Steady State Visual Evoked Potential (SSVEP) rapidly becomes a practical signal in...
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SubjectTerms Accuracy
BCI
Electric potential
Electroencephalography
Feature extraction
Fisher classifier
Hilbert-Huang Transform
SSVEP
Time frequency analysis
Transforms
Visualization
Title Research on SSVEP feature extraction based on HHT
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