2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery · 2010 · 23 citations · 1 references
EngineeringBiomedical EngineeringElectrical EngineeringBiomedical Signal ProcessingFeature EngineeringNeural EngineeringElectroencephalographyEvoked PotentialsFeature ExtractionNeural Signal ProcessingTime-frequency AnalysisVisual Evoked PotentialsClassification AccuracyElectroencephalogram Signal AnalysisElectroencephalography Feature ExtractionEmpirical Mode DecompositionFast Fourier TransformHilbert TransformHilbert-huang TransformTraining Time
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.
1
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Leigh R. Hochberg, Mijail D. Serruya, Gerhard M. Friehs et al. · Nature · 2006 · 3.3K citations