基于结合小波变换与FastICA算法的脑电信号降噪(英文)

该文提出一种结合小波变换(WPT)与快速独立分量分析(Fast ICA)算法的方法来分析脑电信号。首先,原始脑电信号是通过使用WPT分解为三个层。然后,设置第三层最高频率的系数为零,以减少脑电信号的随机噪声,同时尽可能的保留信号的细节。其次,采取快速独立分量分析算法的优势,从脑电信号中分离所有类型的噪声。提出一种准预期值(QEV)的方法确定脑电图信号来自何处。最后,为了检验系统的性能,所有信道的相关信号在快速独立分量分析的输出进行分析。实验结果证实,交叉相关系数是10-15或10-16的量级,几乎可以被视为零。所提出性能良好的方法可以去除脑电信号所有类型的噪声。

Denoising of EEG Signals by Combining Wavelet Packet Transform with FastICA Algorithm

In this paper, an approach of combining Wavelet Packet Transform （WFF） with Fast Independent Component Analysis （FastlCA） algorithms is proposed to analyze the EEG signals. First of all, the original EEG signals are decomposed to three levels by using WPT. Then, set the coefficients of the highest frequency in 3rd level to zero so as to decrease random noise in EEG signals, while preserving the details as much as possible. Next, take advantage of FastICA algorithm to separate all types of noise from EEG signals. A method of Quasi Expected Value （QEV） is proposed to determine where the EEG signal comes from. Finally, in order to check system performance, correlated signals of all channels are analyzed at FastlCA outputs. Experimental results confirmed that the cross- correlation coefficients are of the order of 10^-15 or 10^-16, which can be regarded as zero. This proposed well - performing approach can remove all types of noise in EEG signals.