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MLP for Electroencephalographic Signals Classification Using Different Adaptive Learning Algorithm

  • Roberto Sepúlveda
  • Oscar Montiel
  • Daniel Gutiérrez
  • Gerardo Díaz
  • Oscar Castillo
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 547)

Abstract

For the identification of muscular pain caused by a puncture in the right arm and eye blink, electroencephalographic (EEG) signals are analyzed in the frequency and temporal domain. EEG activity was recorded from 15 subjects in range of 23–25 years of age, while pain is induced and during blinking. On the other hand, EEG was converted from time to frequency domain using the Fast Fourier Transform (FFT) for being classified by an Artificial Neural Network (ANN). Experimental results in the frequency and time domain using five adaptation algorithms show that both neural network architecture proposals for classification produce successful results.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Roberto Sepúlveda
    • 1
  • Oscar Montiel
    • 1
  • Daniel Gutiérrez
    • 1
  • Gerardo Díaz
    • 1
  • Oscar Castillo
    • 2
  1. 1.Instituto Politécnico Nacional, Centro de Investigación y Desarrollo de Tecnología Digital (CITEDI-IPN)TijuanaMexico
  2. 2.Tijuana Institute of TechnologyTijuanaMexico

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