基于小波变换的脑电信号降噪方法的研究

通过小波变换方法对左右手运动想象脑电信号进行降噪.在对各种小波阈值降噪方法(固定阈值形式Sqtwolog及硬阈值法;使用Birge-Massart惩罚函数的阈值形式及软阈值法;使用分层阈值及改进的阈值法)的讨论比较之后,给出一种改进方案.然后对不同降噪方法处理后的脑电信号用概率神经网络进行分类.最后对分类效果进行比较,证明了此改进方案具有可行性.

EEG Signal Denoising Based on Wavelet Transform

In this paper the use of wavelet transformation as a preprocessing tool for EEG signal de-noising is examined and a novel threshold is proposed for wavelet threshold de-noising method. First,hard threshold, soft threshold and the proposed threshold are used for de-noising and decomposing of the EEG data. And then the wavelet coefficient is used as extracted feature set and is fed to a probabilistic neural network classifier to organize the EEG signals into different ac- tivities. At last, the effectiveness of de-noising with different thresholds is examined by the classification result using probabilistic neural network. It is shown that the novel threshold is better than the last two for EEG signal de-noising based on wavelet transform.