Feature Extraction and Classification of EEG Signals. The Use of a Genetic Algorithm for an Application on Alertness Prediction

  • Pierrick Legrand
  • Laurent Vézard
  • Marie Chavent
  • Frédérique Faïta-Aïnseba
  • Leonardo Trujillo


This chapter presents a method to automatically determine the alertness state of humans. Such a task is relevant in diverse domains, where a person is expected or required to be in a particular state of alertness. For instance, pilots, security personnel, or medical personnel are expected to be in a highly alert state, and this method could help to confirm this or detect possible problems. In this work, electroencephalographic (EEG) data from 58 subjects in two distinct vigilance states (state of high and low alertness) was collected via a cap with 58 electrodes. Thus, a binary classification problem is considered. To apply the proposed approach in a real-world scenario, it is necessary to build a prediction method that requires only a small number of sensors (electrodes), minimizing the total cost and maintenance of the system while also reducing the time required to properly setup the EEG cap. The approach presented in this chapter applies a preprocessing method for EEG signals based on the use of discrete wavelet decomposition (DWT) to extract the energy of each frequency in the signal. Then, a linear regression is performed on the energies of some of these frequencies and the slope of this regression is retained. A genetic algorithm (GA) is used to optimize the selection of frequencies on which the regression is performed and to select the best recording electrode. Results show that the proposed strategy derives accurate predictive models of alertness.


Genetic Algorithm Random Forest Wavelet Decomposition Contingent Negative Variation Correct Classification Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to thank Vérane Faure, Julien Clauzel, and Mathieu Carpentier, who collaborated as interns in the research team during the development of parts of this work.


  1. Anderson C, Sijercic Z (1996) Classification of EEG signals from four subjects during five mental tasks. In: Proceedings of the conference on engineering applications in neural networks, London, United Kingdom, pp 407–414Google Scholar
  2. Ben Khalifa K, Bédoui M, Dogui M, Alexandre F (2005) Alertness states classification by SOM and LVQ neural networks. Int J Inf Technol 1:131–134Google Scholar
  3. Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefzbMATHGoogle Scholar
  4. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees., Wadsworth advanced books and softwareCRC Press, Boca RatonzbMATHGoogle Scholar
  5. Broadhursta D, Goodacrea R, Ah Jonesa A, Rowlandb JJ, Kelp DB (1997) Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry. Anal Chim Acta 348:71–86CrossRefGoogle Scholar
  6. Cavill R, Keun HC, Holmes E, Lindon JC, Nicholson JK, Ebbels TM (2009) Genetic algorithms for simultaneous variable and sample selection in metabonomics. Bioinformatics 25:112–118CrossRefGoogle Scholar
  7. Cecotti H, Graeser A (2008) Convolutional neural network with embedded fourier transform for EEG classification. In: International conference on pattern recognition, Tampa, Florida, pp 1–4Google Scholar
  8. Daubechies I (1992) Ten lectures on wavelets. SIAMGoogle Scholar
  9. De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of MichiganGoogle Scholar
  10. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182zbMATHGoogle Scholar
  11. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, BerlinCrossRefGoogle Scholar
  12. Hazarika N, Chen J, Tsoi C, Sergejew A (1997) Classification of EEG signals using the wavelet transform. Sig Process 59:61–72CrossRefzbMATHGoogle Scholar
  13. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  14. Jacobson E (1974) Biologie des motions. Les bases thoriques de la relaxationGoogle Scholar
  15. Jaffard S, Meyer Y (1996) Wavelet methods for pointwise regularity and local oscillations of functions. Mem Amer Math Soc 123(587)Google Scholar
  16. Jasper HH (1958) Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr Clin Neurophysiol 10:1–370CrossRefGoogle Scholar
  17. Lé Cao K-A, Rossouw D, Robert-Granié C, Besse P (2008) Sparse PLS: variable selection when integrating omics data. Stat Appl Genet Mol Biol 7(Article 35)Google Scholar
  18. Legrand P (2004) Débruitage et interpolation par analyse de la régularité Höldérienne. Application à la modélisation du frottement pneumatique-chaussée. PhD thesis, École Centrale de Nantes et Université de NantesGoogle Scholar
  19. Levy Vehel J, Legrand P (2004) Signal and image processing with FracLab. In: Proceedings of 8th international multidisciplinary conference on complexity and fractals in natureGoogle Scholar
  20. Levy Vehel J, Seuret S (2004) The 2-microlocal formalism. Fractal geometry and applications: a jubilee of benoit mandelbrot. In: Proceedings of symposia in pure mathematics, PSPUM, vol 72, pp 153–215Google Scholar
  21. Lin Y-P, Wang C-H, Jung T-P, Wu T-L, Jeng S-K, Duann J-R, Chen J-H (2010) Eeg-based emotion recognition in music listening. IEEE Trans Biomed Eng 57(7):1798–1806CrossRefGoogle Scholar
  22. Mallat S (2008) A wavelet tour of signal processing, 3rd edn. Academic PressGoogle Scholar
  23. Naitoh P, Johnson LC, Lubin A (1971) Modification of surface negative slow potential (CNV) in the human brain after total sleep loss. Electroencephalogr Clin Neurophysiol 30:17–22CrossRefGoogle Scholar
  24. Niedermeyer E, Lopes da Silva F (2005) Electroencephalography, basic principles, clinical applications and related fields, 5th edn.Google Scholar
  25. Obermaier B, Guger C, Neuper C, Pfurtscheller G (2001) Hidden markov models for online classification of single trial EEG data. Pattern Recogn Lett 22:1299–1309CrossRefzbMATHGoogle Scholar
  26. Rosenblith W (1959) Some quantifiable aspects of the electrical activity of the nervous system (with emphasis upon responses to sensory stimuli). Rev Mod Physics 31:532–545CrossRefGoogle Scholar
  27. Schultz JH (1958) Le training autogne. PUFGoogle Scholar
  28. Subasi A, Akin M, Kiymik K, Erogul O (2005) Automatic recognition of vigilance state by using a wavelet-based artificial neural network. Neural Comput Appl 14:45–55CrossRefGoogle Scholar
  29. Tecce JJ (1979) A CNV rebound effect. Electroencephalogr Clin Neurophysiol 46:546–551CrossRefGoogle Scholar
  30. Tenenhaus M (1998) La régression PLS, Théorie et PratiqueGoogle Scholar
  31. Timsit-Berthier M, Gerono A, Mantanus H (1981) Inversion de polarité de la variation contingente négative au cours d’état d’endormissement. EEG Neurophysiol 11:82–88Google Scholar
  32. Vézard L (2010) Réduction de dimension en apprentissage supervisé. applications à l’étude de l’activité cérébrale. Master’s thesis, INSA de Toulouse. Available at the following URL
  33. Vézard L, Legrand P, Chavent M, Faïta Aïnseba F, Clauzel J, Trujillo L (2014) Classification of EEG signals by evolutionary algorithm. Adv Knowl Discov Manage 4:133–153CrossRefGoogle Scholar
  34. Vuckovic A, Radivojevic V, Chen A, Popovic D (2002) Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. Med Eng Phys 24:349–360CrossRefGoogle Scholar
  35. Walter WG, Cooper R, Aldridge V, McCallum WC, Winter A (1964) Contingent negative variation: an electric sign of sensorimotor association and expectancy in the human brain. Nature 203:380–384CrossRefGoogle Scholar
  36. Yeo M, Li X, Shen K, Wilder-Smith E (2009) Can SVM be used for automatic EEG detection of drowsiness? Saf Sci 47:115–124CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Pierrick Legrand
    • 1
  • Laurent Vézard
    • 1
  • Marie Chavent
    • 1
  • Frédérique Faïta-Aïnseba
    • 2
  • Leonardo Trujillo
    • 3
  1. 1.IMB, UMR CNRS 5251, INRIA Bordeaux Sud-OuestUniversity of BordeauxFranceBordeaux
  2. 2.University of BordeauxBordeauxFrance
  3. 3.Instituto Tecnológico de TijuanaTijuanaMéxico

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