脑电Lempel-Ziv复杂度分析的预处理

Preprocessing EEG for Lempel-Ziv Complexity Analysis

目的:探究脑电序列预处理的方法,并研究预处理所导致的脑电序列的Lempel-Ziv复杂度的差异。方法:利用小波包变换去掉脑电序列中易受干扰的低频部分,再通过滤掉脑电序列中的奇异值以达到对脑电序列预处理的目的。选取一段受干扰的脑电子序列,通过图像具体的显示预处理的过程中脑电子序列的变化。对每个受试者,挑选出同一脑电序列中受干扰与未受干扰的脑电子序列,分别计算预处理前后二者的Lempel-Ziv复杂度。任意挑选一段子序列,画出其预处理前后的Lempel-Ziv复杂度曲线。利用统计检验分析预处理所导致的脑电子序列的差异。结果:这种预处理有效地滤掉了脑电序列的噪声干扰和奇异值。统计检验的结果显示:本来具有明显差异的受干扰与未受干扰的脑电子序列,预处理后,二者几乎没有差异了。并且受干扰的脑电子序列预处理后的Lempel-Ziv复杂度接近未受干扰的脑电子序列预处理后的Lempel-Ziv复杂度。结论:对脑电序列进行预处理是必要的。预处理能有效地消除脑电序列中的干扰,同时较好的保留了脑电序列中的有用信息。

Objective： The method of preprocessing EEG is explored to study the difference of Lempel-Ziv Complexity（LZC） of original EEG and preprocessed EEG. Methods： We used wavelet packet transform （WPT） for removing EEG series with low-frequency.Then a nonlinear filter was used to remove ectopic values from EEG.So we achieved the goal of preprocessing EEG. We picked out a EEG subseries polluted by noise and drew it, then drew it through the one-by-one steps of preprocessing to show its changes. We selected a EEG subseries polluted by noise and a EEG subseries unpolluted by noise from the same EEG and worked out LZC of them respectively. We drew LZC of EEG subseries choosed randomly before and after the preprocessing. Then we used statistical test to analyze the difference caused by the preprocessing. Results： The preprocessing denoised EEG and removed ectopic values form EEG effectively, The results of statistical test showed that the sharp difference between the EEG subseries polluted by noise and EEG subseries unpolluted by noise diminished after preprocessing. We also discovered that LZC ofEEG subseries polluted by noise approached LZC ofEEG subseries unpolluted by noise. Conclusions： The results demonstrated the necessity ofpreprocessing EEG. The preprocessing denoised EEG series effectively as well as reserved useful information in EEG preferably.