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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Parvinnia E, Sabeti M, Zolghadri Jahromi M, Boostani R (2014) Classification of EEG signals using adaptive weighted distance nearest neighbor algorithm. J King Saud Univ-Comput Inform Sci 26(1):1–6
Srinivasan V, Eswaran C, Sriraam N (2007) Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inform Technol Biomed 11(3):288–295
Guo L, Wu Y, Cao T, Yan W, Shen X (2011) Classification of mental task from EEG signals using immune feature weighted support vector machines. IEEE Trans Magn 47(5):866–869
Murugavel ASM, Ramakrishnan S (2012) Multi-class SVM for EEG signal classification using wavelet based approximate entropy. CCSIT 2012, pp 335–344
Burke DP, Kelly SP, de Chazal P, Reilly RB, Finucane C (2005) A parametric feature extraction and classification strategy for brain–computer interfacing. IEEE Trans Neural Syst Rehabil Eng 13(1):12–17
Polat K, Günes S (2007) Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput 187(2):1017–1026
Kumar Y, Dewal ML, Anand RS (2014) Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. Signal Image Video Proc 8(7):1323–1334
Subasi A (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32(4):1084–1093
Guo L, Riveero D, Pazaos A (2010) Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 193:156–163
Song Y, Liò P (2010) A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J Biomed Sci Eng 6:556–567
Song Y, Crowcroft J, Zhang J (2012) Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J Neurosci Methods 210:132–146
Zhang J, Wang N, Kuang H, Wang R (2014) An improved method to calculate phase locking value based on Hilbert-Huang transform and its application. Neural Comput Appl 24(1):125–132
Bajaj V, Pachori RB (2012) Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inform Technol Biomed 16(6):1135–1142
Wu HT, Lee CY, Liu CC, Liu AB (2013) 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. Comput Math Methods Med, vol. 2013, Article ID 231762
Ahangi A, Karamnejad M, Mohammadi N, Ebrahimpour R, Bagheri N (2013) Multiple classifier system for EEG signal classification with application to brain–computer interfaces. Neural Comput Appl 23(5):1319–1327
Hu DY, Li W, Chen X (2011) Feature extraction of motor imagery EEG signals based on wavelet packet decomposition. In: Proceedings of the 2011 IEEE/ICME international conference on complex medical engineering, pp 694–697
Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88(6):2297–2301
Richman J, Moorman J (2000) Physiological time series analysis using approximate entropy and sample entropy. Am J Physiol 278(6):2039–2049
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501
Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern 42(2):513–529
Vapnik V (1995) The nature of statistical learning theory. Springer, New York
Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–27. http://www.csie.ntu.edu.tw/~cjlin/libsvm
Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E Stat Nonlinear Soft Matter Phys 64:061907-1–061907-8
Tzallas AT, Tsipouras MG, Fotiadis DI (2007) Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput Intell Neurosci. Article ID 80510
EEG Data. http://www.cs.colostate.edu/eeg/main/data/1989_Keirn_and_Aunon
Geethanjali P, Mohan YK, Jinisha S (2012) Time domain Feature extraction and classification of EEG data for brain computer interface. In: Proceedings of the 9th international conference on fuzzy systems and knowledge discovery (FSKD 2012), May 29–31, 2012, IEEE Press, pp 1136–1139
Acknowledgments
This work is partly supported by National Natural Science Foundation of China (No. 61373127), the China Postdoctoral Science Foundation (No. 20110491530), and the University Scientific Research Project of Liaoning Education Department of China (No. 2011186).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, Y., Zhang, Y., Wang, J. et al. Comparison of classification methods on EEG signals based on wavelet packet decomposition. Neural Comput & Applic 26, 1217–1225 (2015). https://doi.org/10.1007/s00521-014-1786-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-014-1786-7