ive learning algorithm

Roberto Sepúlveda; Oscar Montiel; Daniel Gutiérrez; Gerardo Díaz; Oscar Castillo

doi:10.1007/978-3-319-05170-3_25

ive learning algorithm

Roberto Sepúlveda, Oscar Montiel, Daniel Gutiérrez, Gerardo Díaz, Oscar Castillo

Centro de Investigación y Desarrollo de Tecnología Digital (CITEDI)

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	369-380
Number of pages	12
Journal	Studies in Computational Intelligence
Volume	547
DOIs	https://doi.org/10.1007/978-3-319-05170-3_25
State	Published - 2014

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10.1007/978-3-319-05170-3_25

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title = " ive learning algorithm",

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.",

author = "Roberto Sep{\'u}lveda and Oscar Montiel and Daniel Guti{\'e}rrez and Gerardo D{\'i}az and Oscar Castillo",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.",

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AU - Sepúlveda, Roberto

AU - Montiel, Oscar

AU - Gutiérrez, Daniel

AU - Díaz, Gerardo

AU - Castillo, Oscar

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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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DO - 10.1007/978-3-319-05170-3_25

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SN - 1860-949X

VL - 547

SP - 369

EP - 380

JO - Studies in Computational Intelligence

JF - Studies in Computational Intelligence

ER -

ive learning algorithm

Abstract

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