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In this paper, we investigated EEG feature in the alcoholics and the controls. Principle component analysis was applied to preprocess the original data to reduce the dimensions of EEG. Wavelet transform decomposed the EEGs into five corresponding frequency bands. Power spectrum was estimated in each band. By comparing the power spectrum of the alcoholics(More)
Principal Component Analysis (PCA) is a widely used technology about dimensional reduction. Non-negative Matrix Factorization (NMF), proposed by Lee and Sung, is a new image analysis method. In this paper, PCA and NMF are used to extract facial expression feature, and the recognition results of two methods are compared. We also try to process basic image(More)
To nonstationary characteristics of surface electromyography (sEMG) signals, a novel sEMG pattern recognition method, which is based on wavelet packet transformation and support vector machine (SVM), is proposed. Raw four channels sEMG signals from four corresponding muscles are first analyzed with wavelet packet transformation. And then the energy of(More)
A method based on wavelet transform and support vector machine (SVM) for detecting text under complex background is proposed. First, the image is decomposed by wavelet, and then the texture characteristic of text is extracted by using SVM on low-frequency approximate sub-space and high-frequency energy sub-space. Combining wavelet transform and SVM not only(More)