基于小波包的LMD在EEG信号分析中的研究

Research on LMD in EEG Signal Analysis Based on Wavelet Packet

脑电信号(Electroencephalograph,EEG)是一种产生自脑神经细胞活动的极其微弱的电位反映,同时也是一种非平稳、非线性的电信号。针对脑电信号在采集过程中易受到外界噪声干扰的问题,为了降低脑电信号中噪声的含量,提高脑电信号分解效率,提出了一种基于小波包的局部均值分解(Local Mean Decomposition,LMD)方法。该方法主要利用小波包对采集到的脑电信号进行去噪预处理,再通过局部均值分解进行分析。仿真实验结果表明,采用经过小波包去噪预处理的LMD分解能够有效地去除原始信号中的高频噪声,使得局部均值分解效率提高,且能够有效消除噪声分量对分解过程和结果的影响。

The EEG signal is a very weak reflection of potential which its produced by brain nerve cell activity, at the same time, it＇s also a kind of non-stationary and nonlinear electrical signal. EEG signal are shsceptible to external noise interference in the collection process. In order to reduce the noise levels in the EEG signals and improve the efficiency of EEG signal decomposition, this paper proposed a local mean decomposition me-thod based on wavelet packet. This method mainly USPS wavelet packet to make denoising pretreatment on the collected EEG signals, and then analy- zesitvia local mean decomposition. The simulation results show that the improved LMD decomposition can effectively remove the high frequency noise in the original signals, and can on decomposition process and result. effectively eliminate the influence of noise componenton