基于小波分析的EEG信号自适应去噪的应用研究

介绍了小波变换应用于EEG信号消噪处理中的原理及自适应噪声抵消器的原理。根据短时动态信号与平稳背景噪声的特征区别,对输入混合信号进行白化预处理,以时间序列的AR模型理论为依据,导出背景噪声白化滤波器的结构;将小波变换与自适应滤波相结合,对经白化处理后的信号进行自适应去噪,将去噪后信号及平均信号做了功率谱估计比较,实验结果表明该方法能有效地去除弱信号中的噪声。

Application Research Based on Wavelet Analysis in Removing Noises in EEG Signals Accordingly

This paper introduced the theory of the application of wavelet transform in removing noise in EEG signals and counteracting noises by adaptive filter. In virtue of the difference between short- time dynamic signal and windless background noises,we pretreated the mixed input signals by whitening them. Based on the theory of AR model,we educed the structure of the background noises whiten filter, by combining the wavelet transform and adaptive filtering, the noises were remove from the processed signals through adaptive filter. We also compared the PSE between the output and the average signal. The simulation experiment showed that this method removes noises efficiently in weak signals.