This study aimed to find a good coupling feature extraction method to effectively analyze resting state EEG signals (rsEEG) of amnestic mild cognitive impairment(aMCI) with type 2 diabetes mellitus(T2DM) and normal control (NC) with T2DM. A method of EEG signal coupling feature extraction based on weight permutation conditional mutual information (WPCMI) was proposed in this research. With the WPCMI method, coupling feature strength of two time series in Alpha1, Alpha2, Beta1, Beta2 and Gamma bands for aMCI with T2DM and NC with T2DM could be extracted respectively. Then selected three frequency bands coupling feature matrix with the help of multi-spectral image transformation method to map it as spectral image characteristics. And finally classified these characteristics through the convolution neural network method(CNN). For aMCI with T2DM and NC with T2DM, the highest classification accuracy of 96%, 95%, 95% could be achieved respectively in the combination of three frequency bands (Alpha1, Alpha2, Gamma), (Beta1, Beta2 and Gamma) and (Alpha2, Beta1, Beta2). This WPCMI method highlighted the coupling dynamic characteristics of EEG signals, and its classification performance was better than all previous methods in aMCI with T2DM diagnosis field. WPCMI method could be used as an effective biomarker to distinguish EEG signals of aMCI with T2DM and NC with T2DM. The coupling feature extraction method used in this paper provided a new perspective for the EEG analysis of aMCI with T2DM.

本研究旨在寻找一种良好的耦合特征提取方法来有效分析遗忘型轻度认知障碍（aMCI）合并2型糖尿病（T2DM）和正常对照（NC）合并T2DM的静息态脑电信号（rsEEG）。本研究提出了一种基于权重排列条件互信息（WPCMI）的脑电信号耦合特征提取方法。利用WPCMI方法，可以分别提取aMCI与T2DM和NC与T2DM的Alpha1、Alpha2、Beta1、Beta2和Gamma频段的两个时间序列的耦合特征强度。然后借助多光谱图像变换方法选取三个频段耦合特征矩阵，将其映射为光谱图像特征。最后通过卷积神经网络方法（CNN）对这些特征进行分类。对于带有T2DM的aMCI和带有T2DM的NC，在三个频段（Alpha1、Alpha2、Gamma）、（Beta1、Beta2和Gamma）和（阿尔法 2、贝塔 1、贝塔 2）。该WPCMI方法突出了EEG信号的耦合动态特征，其分类性能优于aMCI与T2DM诊断领域的所有方法。WPCMI方法可作为区分aMCI与T2DM、NC与T2DM脑电信号的有效生物标志物。本文采用的耦合特征提取方法为aMCI与T2DM的脑电图分析提供了新的视角。