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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
Chapter

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.

Keywords

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.

Notes

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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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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