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- W2359366567 abstract "The classification of visual evoked potential(VEP) with wavelet transform and BP neural network for obtaining brain-computer interface(BCI) control signal.One-dimensional discrete wavelet transform(DWT) method are used to extract the low-frequency weak VEP signal from strong background noise.The extracted feature vectors are input to the BP neural network to achieve the recognition of event related potential(ERP).Experiments show that the feature extraction in wavelet transformation can effectively extract VEP features,reduce noise and decrease the dimensionality and the BP neural network can recognize ERP from VEP signal correctly,10 tests,the average correct identification rate of 99.375%,which is useful to produce BCI control signal." @default.
- W2359366567 created "2016-06-24" @default.
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- W2359366567 date "2008-01-01" @default.
- W2359366567 modified "2023-09-22" @default.
- W2359366567 title "The Recognition of Visual Evoked Potential Based on Wavelet and BP Neural Network" @default.
- W2359366567 hasPublicationYear "2008" @default.
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