Multi optimized SVM classifiers for motor imagery left and right hand movement identification

  • Kamel MebarkiaEmail author
  • Aicha Reffad
Scientific Paper


EEG signal can be a good alternative for disabled persons who cannot perform actions or perform them improperly. Brain computer interface (BCI) is an attractive technology which permits control and interaction with a computer or a machine using EEG signals. Brain task identification based on EEG signals is very difficult task and is still challenging researchers. In this paper, the motor imagery of left and right hand actions are identified using new features which are fed to a set of optimized SVM classifiers. Multi classifiers based classification showed having high faculty to improve the classification accuracy when using different kind or diversified features. Features selection was performed by genetic algorithm optimization. In single optimized SVM classifier, a mean classification accuracy of 89.8% was reached. To further improve the rate of classification, three SVMs classifiers have been suggested and optimized in order to find suitable features for each classifier. The three SVMs classifiers were optimized and achieved a performance mean of 94.11%. The achieved performance is a significant improvement comparing to the existing methods which does not exceed 81% while using the same database. Here, combining multi classifiers with selecting suitable features by optimization can be a good alternative for BCI applications.


Electroencephalography EEG signals Motor imagery BCI (MI) Features extraction BCI system SVM classifier Optimization 



The authors would like to acknowledge the Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), Graz University of Technology, for providing the dataset online (

Compliance with ethical standards

Conflict of interest

No conflicts of interest are declared by the authors.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


  1. 1.
    Teplan M (2002) Fundamental of EEG measurement. Meas Sci Rev 2(2):1Google Scholar
  2. 2.
    Nicolas-Alonso LF, Gomez-Gil J (2012) Brain computer interfaces, a review. Sensors 12(2):1211–1279CrossRefPubMedGoogle Scholar
  3. 3.
    Sanei S, Chambers JA (2007) EEG signal processing. Wiley, ChichesterCrossRefGoogle Scholar
  4. 4.
    The Ten Twenty Electrode System: International Federation of Societies for Electroencephalography and Clinical Neurophysiology. Am J EEG Technol 1961; 1:13–19Google Scholar
  5. 5.
    Brodu N, Lotte F, Lécuyer A (2011) Comparative study of band-power extraction techniques for motor imagery classification. In: IEEE symposium on computational intelligence, cognitive algorithms, mind, and brain (CCMB), pp 1–6Google Scholar
  6. 6.
    Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8(4):441–446CrossRefPubMedGoogle Scholar
  7. 7.
    Blankertz B, Tomioka R, Lemm S, Kawanabe M, Muller KR (2008) Optimizing spatial filters for robust EEG single-trial analysis. Signal Process Mag IEEE 25(1):41–56CrossRefGoogle Scholar
  8. 8.
    Ebrahini T, Vesin JM, Garcia G (2003) Brain-computer interface in multimedia communication. IEEE Signal Process Mag 20(1):14–24CrossRefGoogle Scholar
  9. 9.
    Chaudhari R, Galiyawala HJ (2017) A review on motor imagery signal classification for BCI. Signal Process Int J (SPIJ) 11(2):16–34Google Scholar
  10. 10.
    Abdulkader SN, Atia A, Mostafa MSM (2015) Brain computer interfacing: applications and challenges. Egyptian Inform J 16:213–230CrossRefGoogle Scholar
  11. 11.
    Malouin F, Richards CL (2013) Clinical applications of motor imagery in rehabilitation Multisensory imagery. Springer, New York, pp 397–419Google Scholar
  12. 12.
    Middendorf M, McMillan G, Calhoun G, Jones KS (2000) Brain–computer interfaces based on steady-state visual evoked response. IEEE Trans Rehabil Eng 8:211–213CrossRefPubMedGoogle Scholar
  13. 13.
    Kundu S, Ari S (2017) P300 detection with brain-computer interface application using PCA and ensemble of weighted SVMs. IETE J Res 64(3):406–414CrossRefGoogle Scholar
  14. 14.
    Bashashati A, Fatourechi M, Ward RK, Birch GE (2007) A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J Neural Eng 4(2):32–57CrossRefGoogle Scholar
  15. 15.
    Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtschellere G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791CrossRefPubMedGoogle Scholar
  16. 16.
    Lotte F, Congedo M, Léecuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain computer interfaces. J Neural Eng 4(2):R1–R13CrossRefPubMedGoogle Scholar
  17. 17.
    Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F (2018) A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng 15(3):031005CrossRefPubMedGoogle Scholar
  18. 18.
    Pattnaik PK, Sarraf J (2016) Brain computer interface issues on hand movement. J King Saud Univ Comput Inform Sci 30(1):18–24Google Scholar
  19. 19.
    Coyle D, Prasad G, McGinnity TM (2005) A time-frequency approach to feature extraction for a brain-computer interface with a comparative analysis of performance measures. EURASIP J Appl Signal Process 19:3141–3151Google Scholar
  20. 20.
    Brodu N, Lotte F, Lécuyer A (2012) Exploring two novel features for EEG-based brain–computer interfaces: multifractal cumulants and predictive complexity. Neurocomputing 79:87–94CrossRefGoogle Scholar
  21. 21.
    Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain computer communication. Proc IEEE 89(7):1123–1134CrossRefGoogle Scholar
  22. 22.
    Zhong M, Lotte F, Girolami M, Lécuyer A (2008) Classifying EEG for brain computer interfaces using Gaussian processes. Pattern Recogn Lett 29(3):354–359CrossRefGoogle Scholar
  23. 23.
    Pfurtscheller G, Neuper C, Schlogl A, Lugger K (1998) Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans Rehabil Eng 6(3):316–325CrossRefPubMedGoogle Scholar
  24. 24.
    Bashashati H, Ward RK, Birch GE, Bashashati A (2015) Comparing different classifiers in sensory motor brain computer interfaces. PLoS ONE 10(6):e0129435CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Lee H, Choi S (2003) PCA+HMM+SVM for EEG pattern classification. In: Proceedings of the IEEE seventh international symposium on signal processing and its applications, pp 541–544Google Scholar
  26. 26.
    Lin CJ, Hsieh MH (2009) Classification of mental task from EEG data using neural networks based on particle swarm optimization. Neurocomputing 72(4–6):1121–1130CrossRefGoogle Scholar
  27. 27.
    Wang Y, Jung T-P (2012) Improving brain-computer interfaces using independent component analysis. In: Allison B, Dunne S, Leeb R, Del R, Millán J, Nijholt A (eds) Towards practical brain–computer interfaces, vol 67. Springer, Berlin, p 83Google Scholar
  28. 28.
    Chiappa S, Barber D (2006) EEG classification using generative independent component analysis. Neurocomputing 69(7–9):769–777CrossRefGoogle Scholar
  29. 29.
    Garrett D, Peterson DA, Anderson CW, Thaut MH (2003) Comparison of linear nonlinear and feature selection methods for EEG signal classification. IEEE Trans Neural Syst Rehabil Eng 11(2):141–144CrossRefPubMedGoogle Scholar
  30. 30.
    Amarasinghe K, Sivils P, Manic M (2016) EEG feature selection for thought driven robots using evolutionary algorithms. In: 9th IEEE international conference on human system interactions (HSI), pp 355–361Google Scholar
  31. 31.
    Herman P, Prasad G, McGinnity TM, Coyle D (2008) Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification. IEEE Trans Neural Syst Rehabil Eng 16(4):317–326CrossRefPubMedGoogle Scholar
  32. 32.
    Obermaier B, Guger C, Pfurtscheller G (1999) Hidden Markov models used for off line classification of EEG data. Biomed Technik 44(6):158–162CrossRefGoogle Scholar
  33. 33.
    Cai Y, Zhang L, Wenjuan J, Changsheng L (2015) A hybrid SVM/HMM classification method for motor imagery based BCI. J Comput Inform Syst 11(4):1259–1267Google Scholar
  34. 34.
    Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37CrossRefGoogle Scholar
  35. 35.
    Rakotomamonjy A, Guigue V, Mallet G, Alvarado V (2005) Ensemble of svms for improving brain computer interface p300 speller performances. In: International conference on artificial neural networks, pp 45–50Google Scholar
  36. 36.
    Lotte F, Lécuyer A, Lamarche F, Arnaldi B (2007) Studying the use of fuzzy inference systems for motor imagery classification. IEEE Trans Neural Syst Rehabil Eng 15(2):322–332CrossRefGoogle Scholar
  37. 37.
    Lemm S, Schafer C, Curio G (2004) BCI competition 2003–data set III: probabilistic modeling of sensorimotor mu rhythms for classification of imaginary hand movements. IEEE Trans Biomed Eng 51:1077–1080CrossRefPubMedGoogle Scholar
  38. 38.
    Ende FM, Louis AK, Maass P, Mayer-Kress G (1998) EEG signal analysis by continuous wavelet transform techniques. In: Kantz H, Kurths J, Mayer-Kress G (eds) nonlinear analysis of physiological data. Springer, Berlin, pp 213–219CrossRefGoogle Scholar
  39. 39.
    Samar VJ, Bopardikar A, Rao R, Swartz K (1999) Wavelet analysis of neuroelectric waveforms: a conceptual tutorial. Brain Lang 66:7–60CrossRefPubMedGoogle Scholar
  40. 40.
    Akay M (ed) (1997) Time frequency and wavelets in biomedical signal processing, series in biomedical engineering. IEEE Press, PiscatawayGoogle Scholar
  41. 41.
    Hsu WY, Sun YN (2009) EEG-based motor imagery analysis using weighted wavelet transform features. J Neurosci Methods 167(2):310–318CrossRefGoogle Scholar
  42. 42.
    Yang BH, Yan GZ, Yan RG, Wu T (2007) Adaptive subject-based feature extraction in brain-computer interfaces using wavelet packet best basis decomposition. Med Eng Phys 29(1):48–53CrossRefPubMedGoogle Scholar
  43. 43.
    Song A, Xu B (2010) Features extraction of motor imagery EEG based on wavelet transform and higher-order statistics. Int J Wavelets Multiresolut Inf Process 8(3):373–384CrossRefGoogle Scholar
  44. 44.
    Xu Q, Zhou H, Wang Y, Huang J (2009) Fuzzy support vector machine for classification of EEG signals using wavelet-based features. Med Eng Phys 31(7):858–865CrossRefPubMedGoogle Scholar
  45. 45.
    Blankertz B, Müller KR, Curio G, Vaughan TM, Schalk G, Wolpaw JR, Schlögl A, Neuper C, Pfurtscheller G, Hinterberger T, Schröder M, Birbaumer N (2004) The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans Biomed Eng 51(6):1044–1051CrossRefPubMedGoogle Scholar
  46. 46.
    Blankertz B, Muller KR, Krusienski DJ, Schalk G, Wolpaw JR, Schlogl A, Pfurtscheller G, Millan JDR, Schroder M, Birbaumer N (2006) The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng 14(2):153–159CrossRefPubMedGoogle Scholar
  47. 47.
    Higuchi T (1988) Approach to an irregular time series on the basis of the fractal theory. Physica D 31:277–283CrossRefGoogle Scholar
  48. 48.
    Accardo A, Affinito M, Carrozzi M, Bouquet F (1997) Use of the fractal dimension for the analysis of electroencephalographic time series. Biol Cyber 77:339–350CrossRefGoogle Scholar
  49. 49.
    Vapnik VN (1995) The nature of statistical learning theory. ISBN 0-387-94559-8, Springer-VerlagGoogle Scholar
  50. 50.
    Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167CrossRefGoogle Scholar
  51. 51.
    Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140Google Scholar
  52. 52.
    Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169–198CrossRefGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2019

Authors and Affiliations

  1. 1.LIS Laboratory, Electronics Department, Faculty of TechnologySétif 1 UniversitySétifAlgeria
  2. 2.LAS Laboratory, Electrotechnics Department, Faculty of TechnologySétif 1 UniversitySétifAlgeria

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