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.
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