Abstract
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.
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References
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 407–414
Ben Khalifa K, Bédoui M, Dogui M, Alexandre F (2005) Alertness states classification by SOM and LVQ neural networks. Int J Inf Technol 1:131–134
Breiman L (2001) Random forests. Mach Learn 45:5–32
Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees., Wadsworth advanced books and softwareCRC Press, Boca Raton
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 348:71–86
Cavill R, Keun HC, Holmes E, Lindon JC, Nicholson JK, Ebbels TM (2009) Genetic algorithms for simultaneous variable and sample selection in metabonomics. Bioinformatics 25:112–118
Cecotti H, Graeser A (2008) Convolutional neural network with embedded fourier transform for EEG classification. In: International conference on pattern recognition, Tampa, Florida, pp 1–4
Daubechies I (1992) Ten lectures on wavelets. SIAM
De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, Berlin
Hazarika N, Chen J, Tsoi C, Sergejew A (1997) Classification of EEG signals using the wavelet transform. Sig Process 59:61–72
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Jacobson E (1974) Biologie des motions. Les bases thoriques de la relaxation
Jaffard S, Meyer Y (1996) Wavelet methods for pointwise regularity and local oscillations of functions. Mem Amer Math Soc 123(587)
Jasper HH (1958) Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr Clin Neurophysiol 10:1–370
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)
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 Nantes
Levy Vehel J, Legrand P (2004) Signal and image processing with FracLab. In: Proceedings of 8th international multidisciplinary conference on complexity and fractals in nature
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 153–215
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):1798–1806
Mallat S (2008) A wavelet tour of signal processing, 3rd edn. Academic Press
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 30:17–22
Niedermeyer E, Lopes da Silva F (2005) Electroencephalography, basic principles, clinical applications and related fields, 5th edn.
Obermaier B, Guger C, Neuper C, Pfurtscheller G (2001) Hidden markov models for online classification of single trial EEG data. Pattern Recogn Lett 22:1299–1309
Rosenblith W (1959) Some quantifiable aspects of the electrical activity of the nervous system (with emphasis upon responses to sensory stimuli). Rev Mod Physics 31:532–545
Schultz JH (1958) Le training autogne. PUF
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 14:45–55
Tecce JJ (1979) A CNV rebound effect. Electroencephalogr Clin Neurophysiol 46:546–551
Tenenhaus M (1998) La régression PLS, Théorie et Pratique
Timsit-Berthier M, Gerono A, Mantanus H (1981) Inversion de polarité de la variation contingente négative au cours d’état d’endormissement. EEG Neurophysiol 11:82–88
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 http://www.math.u-bordeaux1.fr/archives/stages/laurentvezard2011.pdf
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 4:133–153
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 24:349–360
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 203:380–384
Yeo M, Li X, Shen K, Wilder-Smith E (2009) Can SVM be used for automatic EEG detection of drowsiness? Saf Sci 47:115–124
Acknowledgments
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.
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Legrand, P., Vézard, L., Chavent, M., Faïta-Aïnseba, F., Trujillo, L. (2014). Feature Extraction and Classification of EEG Signals. The Use of a Genetic Algorithm for an Application on Alertness Prediction. In: Miranda, E., Castet, J. (eds) Guide to Brain-Computer Music Interfacing. Springer, London. https://doi.org/10.1007/978-1-4471-6584-2_9
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