针对现有小波熵方法的影响因素分析不明确，影响认知特性的分析结果和可比性，本文确定了分解层数和P_n（第n个子空间信号能量与信号总能量之比）的选择和计算原则，在此基础上结合脑电节律特性，提出了一种基于节律特征的脑电小波熵分析方法，以构成节律信号的所有子空间为单元，统计所有子空间的能量和作为该节律的能量，经过计算得到脑电节律小波熵。基于该方法提取的脑电小波熵值描述了脑电节律信号的分布情况，同时结合小波熵值与脑电节律的联系，分析了小波熵值变化的原因，可追溯到脑电节律信号的变化规律，进而分析相应的认知特性，初步建立了脑电信号—脑电节律—脑电节律小波熵-认知特性的联系。

The analysis of the influencing factors of the wavelet entropy is not simple, which involves the cognitive analysis based on the wavelet entropy of EEG, so some selection and calculation principles of the decomposition level and P_n should be determined. A new wavelet entropy method based on the EEG rhythm characteristics is proposed in this paper, which takes all subspaces of each rhythm as a unit, then calculates the wavelet entropy based on the power of the rhythm. The relationships between the EEG and the wavelet entropy are established by the method. The method is verified in detail by the BCI IV EEG data. Furthermore the relationships between the EEG wavelet entropy and the EEG characteristic are further explored. The method provides an important way for further physiological analysis of the EEG, and the relationships between EEG-rhythm characteristics, EEG wavelet entropy and cognitive features are established by the method.