Abstract
Electroencephalography (EEG) is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG) not only has a close correlation with the human imagination and movement intention but also contains a large amount of physiological or disease information. As a result, it has been fully studied in the field of rehabilitation. To correctly interpret and accurately extract the features of MI-EEG signals, many nonlinear dynamic methods based on entropy, such as Approximate Entropy (ApEn), Sample Entropy (SampEn), Fuzzy Entropy (FE), and Permutation Entropy (PE), have been proposed and exploited continuously in recent years. However, these entropy-based methods can only measure the complexity of MI-EEG based on a single scale and therefore fail to account for the multiscale property inherent in MI-EEG. To solve this problem, Multiscale Sample Entropy (MSE), Multiscale Permutation Entropy (MPE), and Multiscale Fuzzy Entropy (MFE) are developed by introducing scale factor. However, MFE has not been widely used in analysis of MI-EEG, and the same parameter values are employed when the MFE method is used to calculate the fuzzy entropy values on multiple scales. Actually, each coarse-grained MI-EEG carries the characteristic information of the original signal on different scale factors. It is necessary to optimize MFE parameters to discover more feature information. In this paper, the parameters of MFE are optimized independently for each scale factor, and the improved MFE (IMFE) is applied to the feature extraction of MI-EEG. Based on the event-related desynchronization (ERD)/event-related synchronization (ERS) phenomenon, IMFE features from multi channels are fused organically to construct the feature vector. Experiments are conducted on a public dataset by using Support Vector Machine (SVM) as a classifier. The experiment results of 10-fold cross-validation show that the proposed method yields relatively high classification accuracy compared with other entropy-based and classical time–frequency–space feature extraction methods. The t-test is used to prove the correctness of the improved MFE.
Highlights
Stroke is a disease that causes lethal damage to human health
Zhou et al calculated the Sample Entropy (SampEn) of the Motor Imagery EEG (MI-EEG) signal and the classification accuracy was between 50% and 87.8% with a Linear Discriminant Analysis (LDA) classifier [15]; Wang et al used SampEn as the feature of MI-EEG, and the classification rate was between 75.48%
That is because Approximate Entropy (ApEn), SampEn, Fuzzy Entropy (FE), and Permutation Entropy (PE) can only estimate the complexity of time series based on a single scale and Multiscale Sample Entropy (MSE), Multiscale Permutation Entropy (MPE), and Multiscale Fuzzy Entropy (MFE) can measure the complexity of time series on multiple scale factors
Summary
Stroke is a disease that causes lethal damage to human health. These patients often experience motor dysfunction. 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 Their calculations frequently face the problem of insufficient data points. 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 It lacks relative consistency and the result relies heavily on the data length, which is caused by self-matching. Azami et al proposed the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) based on MFE, and applied it to feature extraction on intracranial EEG data and fantasia data; the average classification accuracies on the two datasets were 96% and 75% with a SVM classifier, respectively [33].
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