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Home > Archives > Volume 16, No 6 (2018) > Article

DOI: 10.14704/nq.2018.16.6.1642

Recognition and Feature Extraction of Motor Imagery EEG Signals Based on Complementary Ensemble Empirical Mode Decomposition

Qidong Huang, Min Tang


In view of the characteristics of high nonlinearity and fractional stationarity of motor imagery EEG signals, an improved Complementary Ensemble Empirical Mode Decomposition (CEEMD) method is proposed in this paper. The method does not need to select the basis function in advance and has the characteristics of high self-adaptability. In this paper, Hilbert transform, mutual information, sensitive factor and approximate entropy are used to obtain the time-frequency characteristics, recognition accuracy and other parameters of motor imagery EEG signals in time-frequency domain, and compared with other methods. Using mutual information and sensitive factors, the IMF component with useful information of the original signals can be effectively identified by CEEMD decomposition, and the selected IMF component can be reconstructed and identified by common space model and approximate entropy. The results show that the recognition rate of EEG signal by using the combination of approximate entropy and time-frequency feature is better than that by using time-frequency feature vector alone. Compared with other algorithms, the proposed algorithm has the highest classification accuracy of 84.1%, among 80.9% for ANN algorithm, 79.6% for WT algorithm and 75.8% for EMD-HT algorithm. It indicates that the method in this paper can distinguish the motor imagery tasks, and proves its effectiveness and superiority in the extraction and classification of motor EEG signals.


EEG Signals, Cerebral Nerve, Motor Imagery, Hand and Left Hand Grip Movement Recognition, Brain-Computer Interface, CEEMD-HT, Feature Extraction

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Bajaj V, Pachori RB. Automatic classification of sleep stages based on the time-frequency image of EEG signals. Computer Methods & Programs in Biomedicine 2013; 112(3): 320-28.

Bajaj V, Pachori RB. Eeg signal classification using empirical mode decomposition and support vector machine. Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) 2012; 131: 623-35.

Bajaj V, Pachori RB. Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of eeg signals. Biomedical Engineering Letters 2013;3(1): 17-21.

Birbaumer N, Cohen LG. Brain–computer interfaces: communication and restoration of movement in paralysis. Journal of Physiology 2007; 579(3): 621-36.

Blankertz B, Sannelli C, Halder S, Hammer EM, Kübler A, Müller KR, Curio G, Dickhaus T. Neurophysiological predictor of smr-based bci performance. NeuroImage 2010; 51(4): 1303-09.

Cincotti F, Kauhanen L, Aloise F, Palomäki T, Caporusso N, Jylänki, P, Mattia D, Babiloni F, Vanacker G, Nuttin M, Marciani MG, Millán J R. Vibrotactile feedback for brain-computer interface operation. Computational Intelligence & Neuroscience 2007; 48937:1- 12

Djemili R, Bourouba H, Korba MCA. Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals. Biocybernetics & Biomedical Engineering 2016;36(1): 285-91.

Dong M. Progress on event related potential from sensory stimulation for brain computer interface. Journal of Electronic Measurement & Instrument 2009; 2009(6): 1-6.

Elbeltagy AEHM, Youssef AM, Bayoumy AM, Elhalwagy YZ. Fixed ground-target tracking control of satellites using a nonlinear model predictive control, Mathematical Modeling of Engineering Problems 2018; 5(1): 11-20.

Fu K, Qu J, Chai Y, Dong Y. Classification of seizure based on the time-frequency image of EEG signals using hht and svm. Biomedical Signal Processing & Control 2014; 13(5): 15-22.

Gong P, Chen MY, Zhang L, Jian WJ. Hht-based selection of optimal time-frequency patterns for motor imagery. Applied Mechanics & Materials 2013; 380-384 (22): 3522-25.

Hasan BAS, Gan JQ. Hangman bci: an unsupervised adaptive self-paced brain-computer interface for playing games. Computers in Biology & Medicine 2012: 42(5): 598-606.

Higashi H, Tanaka T. Time sparsification of EEG signals in motor-imagery based brain computer interfaces. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE 2012(4): 4271-74.

Hsu WY. Assembling a multi-feature EEG classifier for left–right motor imagery data using wavelet-based fuzzy approximate entropy for improved accuracy. International Journal of Neural Systems 2015; 25(8): 1550037.

Hsu WY. EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features. Journal of Neuroscience Methods 2010; 189 (2): 295-302.

Hsu WY. Single-trial motor imagery classification using asymmetry ratio, phase relation, wavelet-based fractal, and their selected combination. International Journal of Neural Systems 2013, 23 (2): 1350007.

Hwang HJ, Kwon K, Im CH. Neurofeedback-based motor imagery training for brain-computer interface (bci). Journal of Neuroscience Methods 2009; 179 (1): 150-56.

Kus R, Valbuena D, Zygierewicz J, Malechka T, Graeser A, Durka P. Asynchronous bci based on motor imagery with automated calibration and neurofeedback training. IEEE Transactions on Neural Systems & Rehabilitation Engineering 2012; 20(6): 823-35.

Luo Z, Jia Y. MR image contrast enhancement by wavelet-based contourlet transform. International Journal Bioautomation 2016; 20(2): 265-78

Machado S, Almada LF, Annavarapu RN. Progress and prospects in EEG-based brain-computer interface: clinical applications in neurorehabilitation. Journal of Rehabilitation Robotics 2013; 1: 28-41.

Mak JN, Mcfarland DJ, Vaughan TM, Mccane LM, Tsui PZ, Zeitlin DJ, Sellers EW, Wolpaw JR. EEG correlates of p300-based brain-computer interface (bci) performance in people with amyotrophic lateral sclerosis. Journal of Neural Engineering 2012, 9(2): 026014.

Millán JDR, Rupp R, Müller-Putz GR, Murray-Smith R, Giugliemma C, Tangermann M, Vidaurre C, Cincotti F, Kübler A, Leeb R, Neuper C, Müller KR, Mattia, D. Combining brain–computer interfaces and assistive technologies: state-of-the-art and challenges. Frontiers in Neuroscience 2010; 4(5): 161.

Mousavi EA, Maller JJ, Fitzgerald PB, Lithgow BJ. Wavelet common spatial pattern in asynchronous offline brain computer interfaces. Biomedical Signal Processing & Control 2011; 6(2): 121-28.

Nguyen T, Khosravi A, Creighton D, Nahavandi S. EEG data classification using wavelet features selected by wilcoxon statistics. Neural Computing & Applications 2015; 26(5): 1193-1202.

Ortiz-Rosario A, Adeli H. Brain-computer interface technologies: from signal to action. Reviews in the Neurosciences 2013; 24(5): 537-52.

Pachori RB, Bajaj V. Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Computer Methods & Programs in Biomedicine 2011; 104(3): 373-81.

Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain–computer interfaces for communication and control. Clinical Neurophysiology 2002; 113(6): 767-91.

Rodríguez-Bermúdez G, García-Laencina PJ, Roca-González J, Roca-Dorda J. Efficient feature selection and linear discrimination of EEG signals. Neurocomputing 2013; 115: 161-65.

Rozado D, Duenser A, Howell B. Improving the performance of an EEG-based motor imagery brain computer interface using task evoked changes in pupil diameter. Plos One 2015; 10(3): e0121262.

Tang L, Chen MJ. Image denoising method using the gradient matching pursuit, Mathematical Modelling of Engineering Problems 2016; 3(2): 53-56.

Wang XY, Jin J, Zhang Y, Wang B. Brain control: human-computer integration control based on brain-computer interface approach. Acta Automatica Sinica 2013; 39(3): 208-21.

Yang H., Wu S. EEG classification for bci based on csp and svm-ga. Applied Mechanics & Materials 2013; 459: 228-31.

Yeh CL, Chang HC, Wu CH, Lee PL. Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition. BioMedical Engineering OnLine 2010; 9(1): 25.

Zhang R, Xu P, Liu T, Zhang Y, Guo L, Li P, Yao DZ. Local temporal correlation common spatial patterns for single trial EEG classification during motor imagery. Computational & Mathematical Methods in Medicine 2013: 2013(6725): 591216.

Zou G. Ant colony clustering algorithm and improved markov random fusion algorithm in image segmentation of brain images. International Journal Bioautomation 2016; 20(4): 505-14.