Research on the Method of EEG Signal Denoising Based on the DIVA Model
Zhang Shao-ba · Dianzi xuebao · 2015
There are power frequency interference and other kinds of noise in the electro encephalo gram( EEG) signal acquisition process. They make the signal shownon-stationary and a variety of multi-form waveform in the instantaneous structure. Then such signal will affect the normal processing of the speech in DIVA( Directions Into Velocities of Articulators) model. Therefore,this paper proposes an adaptive sparse decomposition model for the feature extraction of EEG signal structure and makes use of Matching Pursuit algorithm to solve the optimal atom. Then the original EEG signal can be represented by atoms in the complete atomic library. Finally,this model removes noise that exists in the EEG signal and is compared with wavelet transform method. Simulation results showthat after we put the denoising EEG signal into the model,the phonetic pronunciation improves.