Abstract：To eliminate the noise mixed in Electroencephalogram (EEG), an EEG de-noising method is proposed based on double-density discrete wavelet transform using neighbor-dependency thresholding. Firstly, high frequency coefficients of multilayer signals are obtained by double-density discrete wavelet decomposition. Then, the wavelet coefficients are shrunk with neighbor-dependency thresholding algorithm, which takes the statistical dependencies of the wavelet coefficients into account. Finally, the de-noising signal is obtained by reconstructing shrunk wavelet coefficients. The simulation results of the de-noising experiments on standard noise-adding signal and real EEG show that compared to the first generation discrete wavelet algorithm and traditional soft threshold methods, the proposed de-noising algorithm has the benefits of higher SNR, lower RMSE and Errmax.
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2014, Vol. 27 
