Abstract: The uncertainty of the EEG signals independent source number and other noise interference make the acquisition of EEG signals between pilot signal crosstalk, noise source signal is difficult to estimate and mixed, seriously affecting the later analysis and research of EEG signals. The paper combines the wavelet transform with the blind source separation algorithm, and rearranges the cross-term interference phenomenon of the Wigner distribution in the blind source separation algorithm. The main idea of this experiment is to first carry out wavelet transform of each pilot signal, extract the characteristic wave of β wave, and then carry out blind source separation based on rearranged smooth pseudo-Wigner distribution for these β wave signals, and separate the correlation β wave component. The experimental results show that the method used in this paper separates the components of EEG signals with large correlation between them, and solves the problem that the source signal is difficult to estimate and so on, and makes the recognition result be improved obviously.

Key words: Electroencephalogram（EEG）, wavelet transform, blind source separation, rearranged smooth pseudo-Wigner distribution

摘要： 由于脑电信号独立源数目的不确定性以及其他噪声的干扰，使得采集的脑电信号各导信号之间产生串扰、源信号难以估计以及噪声混杂等问题，严重影响了对脑电信号的分析研究。将小波变换与盲源分离算法相结合，并对盲源分离算法中维格纳分布存在的交叉项干扰现象进行重排处理。主要思路是首先将每一导信号进行小波变换，提取出特征波β波，然后对这些β波信号进行基于重排光滑伪维格纳分布的盲源分离，分离出关联性极大的β波成分。实验结果表明，所用方法分离出了各导信号中关联性大的脑电信号成分，并在一定程度上解决了源信号难以估计等问题，使识别结果有明显的提升。

关键词: 脑电信号, 小波变换, 盲源分离, 重排光滑伪维格纳分布