Research on SSVEP feature extraction based on HHT

Author

Zhao, Li ; Yuan, Pengxian ; Xiao, Longteng ; Meng, Qingguo ; Hu, Daofu ; Shen, Hui

Author_Institution

Dept. of Autom. & Electr. Eng., Tianjin Univ. of Technol. & Educ., Tianjin, China

Volume

5

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

2220

Lastpage

2223

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.

Keywords

Hilbert transforms; brain-computer interfaces; eigenvalues and eigenfunctions; visual evoked potentials; Fisher classifier; Hilbert-Huang transformation; SSVEP feature extraction; brain-computer interface system; eigenvector; empirical mode decomposition; extraction method; fast Fourier transform; steady state visual evoked potential; time-frequency processing system; Accuracy; Electric potential; Electroencephalography; Feature extraction; Time frequency analysis; Transforms; Visualization; BCI; Fisher classifier; Hilbert-Huang Transform; SSVEP;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5931-5

Type

conf

DOI

10.1109/FSKD.2010.5569537

Filename

5569537