###### Abstract
With the development of nonlinear dynamics, it has been proved that the brain is a nonlinear dynamic system, and EEG can be considered as the output of the system. To obtain a better classification result, some researchers try to use various complexity measures--for example, dimensions and entropies--to extract the features of EEG signals. However, their calculations frequently face the problem of insufficient data points. Moreover, most defined dimensions and entropies display the limitations of experimental data in the application since all recorded signals are polluted by noise in some way, which prevents accurate estimation. In order to address the insufficient and noisy data problems in physiological signals, [PERSON] [11] put forward Approximate Entropy (ApEn), which can measure the complexity of time series. Once introduced, ApEn has been widely used in physiological signals such as EEG [12;13] and has shown its advantages compared with most complexity measures--for instance, the correlation dimension and the Lyapunov exponent. Nevertheless, it lacks relative consistency and the result relies heavily on the data length, which is caused by self-matching. To tackle these problems, [PERSON] [14] presented Sample Entropy (SampEn), in which there is no self-matching. Once put forward, SampEn has a certain application in the feature extraction of EEG [15;16]. [PERSON] et al. calculated the SampEn of the MI-EEG signal and the classification accuracy was between 50% and 87.8% with a Linear Discriminant Analysis (LDA) classifier [15]; [PERSON] et al. used SampEn as the feature of MI-EEG, and the classification rate was between 75.48% and 78.68% by using Support Vector Machine (SVM) optimized by a Genetic Algorithm (GA) [16]. These applications indicate that SampEn possesses relative consistency and is less dependent on data length. However, the Heaviside function is used to measure the similarity definition of reconstructed vectors in the computation of ApEn and SampEn, and this results in a lack of continuity for both the two statistical measures because of the mutation of the Heaviside function. With regard to this disadvantage, [PERSON] et al. developed a new statistic, Fuzzy Entropy (FE), which can evaluate the self-similarity of time series [17]. Compared with the calculation procedure of ApEn and SampEn, FE replaces the Heaviside function with fuzzy membership function. It not only has stronger relative consistency and is less dependent on data length, but also achieves continuity and more resistance to noise. FE has been widely applied in EEG. [PERSON] et al. extracted the features of MI-EEG signals based on FE and the average classification accuracy was 87.22% by a LDA classifier [18]; [PERSON] et al. made use of FE to extract attention level features from EEG signals and the average identification rate reached 81% with a SVM classifier [19]. In addition, Permutation Entropy (PE), which was introduced by [PERSON] et al. in 2002 [20], can also detect dynamic complexity changes in time series, and it has been widely applied to the analysis of EEGs [21;22]. Meanwhile, PE has some limitations. It is unable to extract the complexity information from data with spiky features or abrupt changes in magnitude and easily ignores the information contained in a small probability event. Subsequently, Weighted-Permutation Entropy (WPE) [23] and Permutation Renyi Entropy (PEr) [24] were introduced to improve the performance of PE and be exploited for the feature extraction of EEG. However, ApEn, SampEn, PE, and FE are single-scale based and therefore fail to account for the multiple scales inherent in brain electrical activities. So, [PERSON] et al. proposed Multiscale Entropy (MSE) by introducing a scale factor on the basis of SampEn [25; 26]. MSE can measure the complexity of time series over multiple scales instead of a single scale and can be used in the EEG signals of sleep staging and fatigue driving [27; 28]. Motivated by the merits of PE and MSE, [PERSON] and [PERSON] put forward Multiscale Permutation Entropy (MPE) [29].