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Neural Computing and Applications

, Volume 29, Issue 10, pp 857–863 | Cite as

An entropy fusion method for feature extraction of EEG

  • Shunfei Chen
  • Zhizeng Luo
  • Haitao Gan
Original Article

Abstract

Feature extraction is a vital part in EEG classification. Among the various feature extraction methods, entropy reflects the complexity of the signal. Different entropies reflect the characteristics of the signal from different views. In this paper, we propose a feature extraction method using the fusion of different entropies. The fusion can be a more complete expression of the characteristic of EEG. Four entropies, namely a measure for amplitude based on Shannon entropy, a measure for phase synchronization based on Shannon entropy, wavelet entropy and sample entropy, are firstly extracted from the collected EEG signals. Support vector machine and principal component analysis are then used for classification and dimensionality reduction, respectively. We employ BCI competition 2003 dataset III to evaluate the method. The experimental results show that our method based on four entropies fusion can achieve better classification performance, and the accuracy approximately reaches 88.36 %. Finally, it comes to the conclusion that our method has achieved good performance for feature extraction in EEG classification.

Keywords

EEG Feature extraction Entropy Feature fusion 

Notes

Acknowledgments

The work is supported by National Natural Science Foundation of China (Nos. 61172134, 61671197 and 61601162).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Nguyen T, Khosravi A, Creighton D (2015) Fuzzy system with tabu search learning for classification of motor imagery data. Biomed Signal Process Control 20:61–70CrossRefGoogle Scholar
  2. 2.
    Nguyen T, Khosravi A, Creighton D et al (2015) EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems. Expert Syst Appl 42(9):4370–4380CrossRefGoogle Scholar
  3. 3.
    Torres-García AA, Reyes-García CA, Villaseñor-Pineda L et al (2016) Implementing a fuzzy inference system in a multi-objective EEG channel selection model for imagined speech classification. Expert Syst Appl 59:1–12CrossRefGoogle Scholar
  4. 4.
    Liang S, Choi KS, Qin J, Pang WM, Wang Q, Heng PA (2016) Improving the discrimination of hand motor imagery via virtual reality based visual guidance. Comput Methods Programs Biomed 132:63–74CrossRefGoogle Scholar
  5. 5.
    Gaur P, Pachori RB, Wang H et al (2015) An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface. In: Proceeding of 2015 international joint conference on neural networks (IJCNN) IEEE, pp 1–7Google Scholar
  6. 6.
    Marchesotti S, Bassolino M, Serino A et al (2016) Quantifying the role of motor imagery in brain-machine interfaces. Scientific reports, p 6Google Scholar
  7. 7.
    Hu S, Tian Q, Cao Y et al (2013) Motor imagery classification based on joint regression model and spectral power. Neural Comput Appl 23(7–8):1931–1936CrossRefGoogle Scholar
  8. 8.
    Chen L, Zhao Y, Zhang J et al (2015) Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning. Expert Syst Appl 42(21):7344–7355CrossRefGoogle Scholar
  9. 9.
    Aydın S, Saraoğlu HM, Kara S (2009) Log energy entropy-based EEG classification with multilayer neural networks in seizure. Ann Biomed Eng 37(12):2626–2630CrossRefGoogle Scholar
  10. 10.
    Sushkova OS, Gabova AV, Karabanov AV et al (2015) Time–frequency analysis of simultaneous measurements of electroencephalograms, electromyograms, and mechanical tremor under Parkinson disease. J Commun Technol Electron 60(10):1109–1116CrossRefGoogle Scholar
  11. 11.
    Xiao D, Mu Z, Hu J (2009) Classification of motor imagery EEG signals based on energy entropy. In: Proceeding of 2009 international symposium on intelligent ubiquitous computing and education, pp 61–64Google Scholar
  12. 12.
    Rui Z, Peng X, Rui C et al (2015) Predicting inter-session performance of SMR-based brain-computer interface using the spectral entropy of resting-state EEG. Brain Topogr 28(5):1–11Google Scholar
  13. 13.
    Tass P, Rosenblum MG, Weule J et al (1998) Detection of n:m phase locking from noisy data: application to magnetoencephalography. Phys Rev Lett 81(15):3291CrossRefGoogle Scholar
  14. 14.
    Guo L, Rivero D, Pazos A (2010) Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 193(1):156–163CrossRefGoogle Scholar
  15. 15.
    Zhang Z, Zhou Y, Chen Z et al (2013) Approximate entropy and support vector machines for electroencephalogram signal classification. Neural Regen Res 8(20):1844Google Scholar
  16. 16.
    Chen Z, Zhou H, Zhao L (2011) Decoding human right and left hand motor imagery from EEG single trials using sample entropy. In: Proceeding of 2011 international conference on IEEE electronics and optoelectronics (ICEOE), pp V4-353–V4-356Google Scholar
  17. 17.
    Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36(2):2027–2036CrossRefGoogle Scholar
  18. 18.
    Sharma R, Pachori RB, Acharya UR (2015) Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17(2):669–691CrossRefGoogle Scholar
  19. 19.
    Acharya UR, Yanti R, Zheng JW et al (2013) Automated diagnosis of epilepsy using CWT, HOS and texture parameters. Int J Neural Syst 23(03):1350009CrossRefGoogle Scholar
  20. 20.
    Kannathal N, Choo ML, Acharya UR et al (2005) Entropies for detection of epilepsy in EEG. Comput Methods Programs Biomed 80(3):187–194CrossRefGoogle Scholar
  21. 21.
    Sakihara K, Inagaki M (2015) Mu rhythm desynchronization by tongue thrust observation. Front Hum Neurosci 9:1–10CrossRefGoogle Scholar
  22. 22.
    Kwon G, Kim MY, Lim S et al (2015) Frontoparietal EEG alpha-phase synchrony reflects differential attentional demands during word recall and oculomotor dual-tasks. NeuroReport 26(18):1161–1167CrossRefGoogle Scholar
  23. 23.
    Mormann F, Andrzejak RG, Elger CE et al (2007) Seizure prediction: the long and winding road. Brain 130(2):314–333CrossRefGoogle Scholar
  24. 24.
    Tuncay C (2010) Entropy analyses of spatiotemporal synchronizations in brain signals from patients with focal epilepsies. arXiv preprint. arXiv:1002.3552
  25. 25.
    Bashar SK, Bhuiyan MIH (2015) Automatic feature selection based motor imagery movements detection scheme from EEG signals in the dual tree complex wavelet transform domain. In: Proceeding of 2015 IEEE international conference on telecommunications and photonics (ICTP), pp 1–5Google Scholar
  26. 26.
    Li X, Cui W, Li C (2012) Research on classification method of wavelet entropy and fuzzy neural networks for motor imagery EEG. In: Proceeding of 2012 IEEE international conference on modelling, identification & control (ICMIC), pp 478–482Google Scholar
  27. 27.
    Acharya UR, Fujita H, Sudarshan VK et al (2015) Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl Based Syst 88:85–96CrossRefGoogle Scholar
  28. 28.
    Kumar SP, Sriraam N, Benakop PG et al (2010) Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Syst Appl 37(4):3284–3291CrossRefGoogle Scholar
  29. 29.
    Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049CrossRefGoogle Scholar
  30. 30.
    Nicolaou N, Georgiou J (2012) Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst Appl 39(1):202–209CrossRefGoogle Scholar
  31. 31.
  32. 32.
    Wang L, Xu G, Yang S et al (2012) Motor imagery BCI research based on sample entropy and SVM. In: Proceeding of 2012 sixth international conference on electromagnetic field problems and applications (ICEF), pp 1–4Google Scholar
  33. 33.
    Imran SM, Talukdar MTF, Sakib SK et al (2014) Motor imagery EEG signal classification scheme based on wavelet domain statistical features. In: Proceeding of 2014 international conference on electrical engineering and information communication technology (ICEEICT), pp 1–4Google Scholar
  34. 34.
    Gupta SS, Soman S, Raj PG et al (2014) Improved classification of motor imagery datasets for BCI by using approximate entropy and WOSF features. In: Proceeding of 2014 international conference on signal processing and integrated networks (SPIN), pp 90–94Google Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.Institute of Intelligent Control and RoboticsHangzhou Dianzi UniversityHangzhouChina

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