Feature Extraction Method of sEMG Based on Auto Permutation Entropy
XIE Ping, WEI Xiu-Li, DU Yi-Hao, CHEN Xiao-Ling
Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, College of Electrical Engineering, Yanshan University, Qinhuangdao 066004
Abstract Due to the nonstationarity and the nonlinearity of the surface electromyogram (sEMG), a method based on the permutation entropy and auto mutual information is proposed to quantitatively describe the internal dynamic features of the sEMG and realize the description of nonlinear characteristics under different motion states. Experiments are carried out to acquire the sEMG data of elbow joint at different bending angles. The auto permutation entropies of the signals are calculated and used as the inputs of support vector machines to identify different motion states. The validity of the proposed method is verified by the comparative analysis of auto permutation entropy and other indexes describing the sEMG features.
[1] Bu N, Okamoto M, Tsuji T. A Hybrid Motion Classification Approach for EMG-Based Human-Robot Interfaces Using Bayesian and Neural Networks. IEEE Trans on Robotics, 2009, 25(3): 502-511 [2] Li N, Jiang L, Wang X Q, et al. Design of a 5-DOF Prosthetic Hand with Grasping Force Sensory Feedback. High Technology Letters, 2011, 21(4): 392-397 (in Chinese) (李 楠,姜 力,王新庆,等.具有握力感觉反馈功能的五自由度假手系统设计.高技术通讯, 2011, 21(4): 392-397) [3] Maeshima S, Osawa A, Nishio D, et al. Efficacy of a Hybrid Assistive Limb in Post-Stroke Hemiplegic Patients: A Preliminary Report. BMC Neurology, 2011.DOI: 10.1186/1471-2377-11-116 [4] Khokhar Z O, Xiao Z G, Menon C. Surface EMG Pattern Recognition for Real-Time Control of a Wrist Exoskeleton [EB/OL].[2013-02-10]. http:// link.springer.com/article/10.1186%2F1475-925X-9-41 [5] Luo Z Z, Ma W J, Meng M. Pattern Recognition of Hand Motions Based on HHT and AR-Model. Pattern Recognition and Artificial Intelligence, 2008, 21(2): 227-232 (in Chinese) (罗志增,马文杰,孟 明.一种基于HHT和AR模型的手部运动模式识别方法.模式识别与人工智能, 2008, 21(2): 227-232) [6] Zhang X, Zhou P. Sample Entropy Analysis of Surface EMG for Improved Muscle Activity Onset Detection against Spurious Background Spikes. Journal of Electromyography and Kinesiology, 2012, 22(6): 901-907 [7] Popovic M R, Keller T, Papas I P I, et al. Surface-Stimulation Technology for Grasping and Walking Neuroprostheses. IEEE Engineering in Medicine and Biology Magazine, 2001, 20(1): 82-93 [8] Cao Y H, Tung W W, Gao J B, et al. Detecting Dynamical Changes in Time Series Using the Permutation Entropy[EB/OL]. [2013-05-01]. http://web.ics.purdue.edu/~wwtung/2004_CaoTungGaoPH.pdf [9] Taherkhani F, Rahmani M, Taherkhani F, et al. Permutation Entropy and Detrend Fluctuation Analysis for the Natural Complexity of Cardiac Heart Interbeat Signals. Physica A: Statistical Mechanics and Its Applications, 2013, 392(14): 3106-3112 [10] Li X L, Ouyang G X. Estimating Coupling Direction between Neuronal Populations with Permutation Conditional Mutual Information. NeuroImage, 2010, 52(2): 497-507 [11] Yan Z G, Wang Z Z, Ren X M. The Application of the ICA and the Wavelet Entropy in Motion Recognition. Beijing Biomedical Engineering, 2006, 25(5): 457-465 (in Chinese) (颜志国,王志中,任晓梅.独立分量分析和小波熵在动作模式分类中的应用.北京生物医学工程, 2006, 25(5): 457-465) [12] Hoyer D, Frank B, Pompe B, et al. Analysis of Complex Physiological Systems by Information Flow: A Time Scale-Specific Complexity Assessment. Biomedical Engineering, 2006, 51(2): 41-48 [13] Meng M, Luo Z Z. Hand Motion Classification Based on Eye-Moving Assisted EEG. Pattern Recognition and Artificial Intelligence, 2012, 25(6): 1007-1012 (in Chinese) (孟 明,罗志增.基于眼动辅助脑电信号的手部动作分类方法.模式识别与人工智能, 2012, 25(6): 1007-1012)
2014, Vol. 27 
