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A New Signal Processing Approach for Discrimination of EEG Recordings

Author

Listed:
  • Hossein Hassani

    (Research Institute for Energy Management and Planning of University of Tehran, No. 9, Ghods St., Enghelab St., Tehran 1417466191, Iran)

  • Mohammad Reza Yeganegi

    (Department of Accounting, Islamic Azad University, Central Tehran Branch, Tehran 1955847781, Iran)

  • Emmanuel Sirimal Silva

    (Fashion Business School, London College of Fashion, University of the Arts London, 272 High Holborn, London WC1V 7EY, UK)

Abstract

Classifying brain activities based on electroencephalogram (EEG) signals is one of the important applications of time series discriminant analysis for diagnosing brain disorders. In this paper, we introduce a new method based on the Singular Spectrum Analysis (SSA) technique for classifying brain activity based on EEG signals via an application into a benchmark dataset for epileptic study with five categories, consisting of 100 EEG recordings per category. The results from the SSA based approach are compared with those from discrete wavelet transform before proposing a hybrid SSA and principal component analysis based approach for improving accuracy levels further.

Suggested Citation

  • Hossein Hassani & Mohammad Reza Yeganegi & Emmanuel Sirimal Silva, 2018. "A New Signal Processing Approach for Discrimination of EEG Recordings," Stats, MDPI, vol. 1(1), pages 1-14, November.
    • Handle: RePEc:gam:jstats:v:1:y:2018:i:1:p:11-168:d:181557
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    References listed on IDEAS

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    1. Emmanuel Sirimal Silva & Hossein Hassani, 2015. "On the use of singular spectrum analysis for forecasting U.S. trade before, during and after the 2008 recession," International Economics, CEPII research center, issue 141, pages 34-49.
    2. G. P. Nason & R. Von Sachs & G. Kroisandt, 2000. "Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 271-292.
    3. Mansi Ghodsi & Hossein Hassani & Donya Rahmani & Emmanuel Sirimal Silva, 2018. "Vector and recurrent singular spectrum analysis: which is better at forecasting?," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(10), pages 1872-1899, July.
    4. Emmanuel Sirimal Silva & Hossein Hassani & Saeed Heravi, 2018. "Modeling European industrial production with multivariate singular spectrum analysis: A cross†industry analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 371-384, April.
    5. Shumway, Robert H., 2003. "Time-frequency clustering and discriminant analysis," Statistics & Probability Letters, Elsevier, vol. 63(3), pages 307-314, July.
    6. Sakiyama, Kenji & Taniguchi, Masanobu, 2004. "Discriminant analysis for locally stationary processes," Journal of Multivariate Analysis, Elsevier, vol. 90(2), pages 282-300, August.
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    8. J. Chacón & T. Duong, 2010. "Multivariate plug-in bandwidth selection with unconstrained pilot bandwidth matrices," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(2), pages 375-398, August.
    9. Maharaj, Elizabeth A. & Alonso, Andres M., 2007. "Discrimination of locally stationary time series using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 879-895, October.
    10. Fryzlewicz, Piotr & Ombao, Hernando, 2009. "Consistent Classification of Nonstationary Time Series Using Stochastic Wavelet Representations," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 299-312.
    11. Hassani, Hossein & Silva, Emmanuel Sirimal & Antonakakis, Nikolaos & Filis, George & Gupta, Rangan, 2017. "Forecasting accuracy evaluation of tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 112-127.
    12. repec:cii:cepiei:2015-q1-141-3 is not listed on IDEAS
    13. Maharaj, Elizabeth Ann & Alonso, Andrés M., 2014. "Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 67-87.
    14. Hsiao-Yun Huang & Hernando Ombao & David S. Stoffer, 2004. "Discrimination and Classification of Nonstationary Time Series Using the SLEX Model," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 763-774, January.
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    Cited by:

    1. Hossein Hassani & Mahdi Kalantari & Zara Ghodsi, 2019. "Evaluating the Performance of Multiple Imputation Methods for Handling Missing Values in Time Series Data: A Study Focused on East Africa, Soil-Carbonate-Stable Isotope Data," Stats, MDPI, vol. 2(4), pages 1-11, December.

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