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Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms

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Abstract

In this paper, we propose a method for the analysis and classification of electroencephalogram (EEG) signals using EEG rhythms. The EEG rhythms capture the nonlinear complex dynamic behavior of the brain system and the nonstationary nature of the EEG signals. This method analyzes common frequency components in multichannel EEG recordings, using the filter bank signal processing. The mean frequency (MF) and RMS bandwidth of the signal are estimated by applying Fourier-transform-based filter bank processing on the EEG rhythms, which we refer intrinsic band functions, inherently present in the EEG signals. The MF and RMS bandwidth estimates, for the different classes (e.g., ictal and seizure-free, open eyes and closed eyes, inter-ictal and ictal, healthy volunteers and epileptic patients, inter-ictal epileptogenic and opposite to epileptogenic zone) of EEG recordings, are statistically different and hence used to distinguish and classify the two classes of signals using a least-squares support vector machine classifier. Experimental results, with 100 % classification accuracy, on a real-world EEG signals database analysis illustrate the effectiveness of the proposed method for EEG signal classification.

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References

  1. R.G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, C.E. Elger, Indications of nonlinear deterministic and finite dimensional structures in time-series of brain electrical activity: dependence on recording region of brain state. Phys. Rev. E 64, 061907 1–061907 7 (2001)

    Article  Google Scholar 

  2. V. Bajaj, R.B. Pachori, Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans. Inf. Technol. Biomed. 16(6), 1135–1142 (2012)

    Article  Google Scholar 

  3. M.C. Casdaglia, L.D. Iasemidisc, R.S. Savita, R.L. Gilmorec, S.N. Roperd, J.C. Sackellaresc, Non-linearity in invasive EEG recordings from patients with temporal lobe epilepsy. Electroencephalogr. Clin. Neurophysiol. 102, 98–105 (1997)

    Article  Google Scholar 

  4. R. Fletcher, Practical Methods of Optimization (Wiley, New York, 1987)

    MATH  Google Scholar 

  5. H.K. Garg, A.K. Kohli, EEG spike detection technique using output correlation method: a kalman filtering approach. Circuits Syst. Signal Process. (2015). doi:10.1007/s00034-015-9982-y

  6. T. Gautama, D.P. Mandic, M.M. Van Hulle, Indications of nonlinear structures in brain electrical activity. Phys. Rev. E 67(4), 0462041–0462045 (2003)

    Article  Google Scholar 

  7. L. Guo, D. Rivero, J. Dorado, J.R. Rabunal, A. Pazos, Automatic epileptic seizure detection in EEG based on line length feature and artificial neural network. J. Neurosci. Methods 191, 101–109 (2010)

    Article  Google Scholar 

  8. N.E. Huang, Z. Shen, S. Long, M. Wu, H. Shih, Q. Zheng, N. Yen, C. Tung, H. Liu, The empirical mode decomposition and Hilbert spectrum for non-linear and non-stationary time series analysis. Proc. R. Soc. A 454, 903–995 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  9. A.H. Khandoker, D.T.H. Lai, R.K. Begg, M. Palaniswami, Wavelet based feature extraction for support vector machines for screening balance impairments in the elderly. IEEE Trans. Neural Syst. Rehabil. Eng. 15(4), 587–597 (1999)

  10. Y. Kumar, M.L. Dewal, R.S. Anand, Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. Signal Image Video Process. 8, 1323–1334 (2014)

    Article  Google Scholar 

  11. S. Mihandoost, M.C. Amirani, EEG signal analysis using spectral correlation function & GARCH model. Signal Image Video Process. (2014). doi:10.1007/s11760-013-0600-9

  12. N. Nicolaou, J. Georgiou, Detection of epileptic electroencephalogram based on permutation entropy and support vector machine. Expert Syst. Appl. 39, 202–209 (2012)

    Article  Google Scholar 

  13. E. Niedermeyer, F.L. da Silva, in Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (Lippincott Williams & Wilkins, Philadelphia, 2004). ISBN: 0-7817-5126-8

  14. J. Nocedal, S.J. Wright, Numerical Optimization (Springer, New York, 1999)

    Book  MATH  Google Scholar 

  15. H. Ocak, Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Signal Process. 88, 1858–1867 (2008)

  16. R.B. Pachori, Discrimination between Ictal and seizure-free EEG signals using empirical mode decomposition. Res. Lett. Signal Process. (2008). doi:10.1155/2008/293056

  17. N.U. Rehman, D.P. Mandic, Multivariate empirical mode decomposition. Proc. R. Soc. A 466, 1291–1302 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  18. N.U. Rehman, Y. Xia, D.P. Mandic, in Application of Multivariate Empirical Mode Decomposition for Seizure Detection in EEG Signals. Proceedings of the Conference in IEEE Engineering Medicine and Biology Society, EMBS Buenos Aires, Argentina, August 31–September 4 (2010)

  19. P. Singh, P.K. Srivastava, R.K. Patney, S.D. Joshi, K. Saha, in Nonpolynomial Spline Based Empirical Mode Decomposition. International Conference of the Signal Processing and Communication, pp. 435–440 (2013)

  20. P. Singh, R.K. Patney, S.D. Joshi, K. Saha, Some studies on nonpolynomial interpolation and error analysis. Appl. Math. Comput. 244, 809–821 (2014)

    MathSciNet  MATH  Google Scholar 

  21. A. Subasi, M.I. Gursoy, EEG signal classification using PCA, ICA, LDA and support vector machine. Expert Syst. Appl. 37, 8659–8666 (2010)

    Article  Google Scholar 

  22. J.A.K. Suykens, J. Vandewalle, Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  23. E.D. Ubeyli, Least square support vector machine employing model-based methods coefficients for analysis of EEG signals. Expert Syst. Appl. 37, 233–239 (2010)

    Article  Google Scholar 

  24. G.T. Van, J.A.K. Suykens, B. Baesens, S. Viaene, J. Vanthienen, G. Dedene, B. De Moor, J. Vandewalle, Benchmarking least squares support vector machine classifiers. Mach. Learn. 54(1), 5–32 (2001)

    MATH  Google Scholar 

  25. V. Vapnik, The Nature of Statistical Learning Theory (Springer, NewYork, 1995)

    Book  MATH  Google Scholar 

  26. J. Ville, Theorie et application de la notion de signal analytic, Cables et Transmissions, 2A(1), 61–74 (translation by I. Selin, Theory and applications of the notion of complex signal, Report T-92, RAND Corporation, Santa Monica, CA (1948))

  27. Z. Wu, N.E. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(1), 1–41 (2009)

    Article  Google Scholar 

  28. S. Xavier-de-Souza, J.A.K. Suykens, J. Vandewalle, D. Bolle, Coupled simulated annealing. IEEE Trans. Syst. Man Cybern. B 40(2), 320–335 (2010)

    Article  Google Scholar 

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Acknowledgments

The authors would like to express sincere thanks to the anonymous reviewers for their valuable suggestions. The authors would like to thank JIIT Noida, for permitting to carry out research at IIT Delhi and providing all required resources throughout this study.

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Authors and Affiliations

  1. Department of EE, Indian Institute of Technology, Delhi, 110016, India

    Pushpendra Singh, S. D. Joshi & R. K. Patney

  2. Samsung R&D Institute India, Noida, 201303, India

    Kaushik Saha

  3. Jaypee Institute of Information Technology, Noida, 201301, India

    Pushpendra Singh

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  1. Pushpendra Singh
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  2. S. D. Joshi
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  4. Kaushik Saha
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Correspondence to Pushpendra Singh.

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Singh, P., Joshi, S.D., Patney, R.K. et al. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst Signal Process 35, 3700–3715 (2016). https://doi.org/10.1007/s00034-015-0225-z

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