小波和希尔伯特变换在脑电信号消噪中的对比研究

为了更好地消除混杂在脑电信号中的噪声，完成脑电分析，对小波和希尔伯特变换（HHT）的脑电信号消噪效果进行了对比研究。在HHT消噪方法中，利用经验模态分解（EMD）算法对脑电信号进行8尺度分解，得到固有模态函数（IMF）分量的组合，经过滤波和信号重构，得到消噪后的脑电信号。实验结果表明，HHT方法能较好地去除脑电信号中的噪声。运用评价准则比较了HHT方法和小波变换方法的消噪效果，发现HHT方法的脑电信号消噪效果优于传统的小波变换消噪，且算法的效率更高。

The Comparative Research on Wavelet and Hilbert Transform in the EEG De-noising

To eliminate the noises mixed in the EEG effectively for completing the EEG analysis, a comparative research is made about the EEG de-noising effects of wavelet and Hilbert transform.In the HHT de noising method, using empirical mode decomposition algorithm to have 8 scales of decomposition for the EEG, and get the combination of components of intrinsic mode functions, then reconstruct the filtered signal, obtain the EEG after de noising finallyExperimental results show that HHT method can properly remove the noises which contained in the EEGThe de noising effects of HHT method and the wavelet transform method are compared by using the evaluation indexes.It finds that HHT method is better than the traditional wavelet transform in the EEG de-nosing and the efficiency of the HHT method is higher.