基于小波变换的盲信号分离的神经网络方法

A Nueral Network Method of Blind Signal Separation Based on Wavelet Transform

提出一种新的盲信号分离的神经网络方法,该方法将小波变换和独立分量分析(ICA,Independent Component Analysis)相结合。利用小波变换的滤噪作用,将混合在原始信号中的部分高频噪声滤除后,再重构原始信号作为ICA的输入信号,有效地克服了现有ICA算法不能将噪声从源信号中分离的缺陷。实验结果表明,将该方法用于多通道脑电信号的盲分离是很有效的。

A neural-network method of blind signal separation is proposed, which combines the wavelet transform with the independent component analysis (ICA). By using the noise filtering function of wavelet transform, some high-frequency noises are removed from the original signals, and the original signals are reconstructed again for the input of ICA. So the defect that ICA is impossible to distinguish noises from source signals can be overcomed effectively. The experimental results show that this method is an effective way to BSS of multi-channel EEC. ©, 2005, Science Press. All right reserved.