Comparison of classification methods on EEG signals based on wavelet packet decomposition

Abstract EEG signals play an important role in both the diagnosis of neurological diseases and understanding the psychophysiological processes. Classification of EEG signals includes feature extraction and feature classification. This paper uses approximate entropy and sample entropy based on wavelet package decomposition as the feature exaction methods and employs support vector machine and extreme learning machine as the classifiers. Experiments are performed in epileptic EEG data and five mental tasks, respectively. Experimental results show that the combination strategy of sample entropy and extreme learning machine has shown great performance, which obtains good classification accuracy and low training time.

[1]  P. Geethanjali,et al.  Time domain Feature extraction and classification of EEG data for Brain Computer Interface , 2012, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery.

[2]  Daniel Rivero,et al.  Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks , 2010, Journal of Neuroscience Methods.

[3]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Jin Zhang,et al.  An improved method to calculate phase locking value based on Hilbert–Huang transform and its application , 2013, Neural Computing and Applications.

[5]  Xi Chen,et al.  Feature extraction of motor imagery EEG signals based on wavelet packet decomposition , 2011, The 2011 IEEE/ICME International Conference on Complex Medical Engineering.

[6]  M. L. Dewal,et al.  Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network , 2012, Signal, Image and Video Processing.

[7]  M. Ahemad,et al.  Mechanisms and applications of plant growth promoting rhizobacteria: Current perspective , 2014 .

[8]  Hsien-Tsai Wu,et al.  Multiscale Cross-Approximate Entropy Analysis as a Measurement of Complexity between ECG R-R Interval and PPG Pulse Amplitude Series among the Normal and Diabetic Subjects , 2013, Comput. Math. Methods Medicine.

[9]  S. Ramakrishnan,et al.  Multi-class SVM for EEG Signal Classification Using Wavelet Based Approximate Entropy , 2012 .

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Kemal Polat,et al.  Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform , 2007, Appl. Math. Comput..

[12]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Nasour Bagheri,et al.  Multiple classifier system for EEG signal classification with application to brain–computer interfaces , 2012, Neural Computing and Applications.

[14]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  J. Crowcroft,et al.  Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine , 2012, Journal of Neuroscience Methods.

[16]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[17]  Elham Parvinnia,et al.  Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm , 2014, J. King Saud Univ. Comput. Inf. Sci..

[18]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[19]  Dimitrios I. Fotiadis,et al.  Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks , 2007, Comput. Intell. Neurosci..

[20]  Pietro Liò,et al.  A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine , 2010 .

[21]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[22]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[23]  Ram Bilas Pachori,et al.  Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition , 2012, IEEE Transactions on Information Technology in Biomedicine.

[24]  V. Srinivasan,et al.  Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks , 2007, IEEE Transactions on Information Technology in Biomedicine.

[25]  Youxi Wu,et al.  Classification of Mental Task From EEG Signals Using Immune Feature Weighted Support Vector Machines , 2011, IEEE Transactions on Magnetics.

[26]  P. de Chazal,et al.  A parametric feature extraction and classification strategy for brain-computer interfacing , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.