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Low Complexity Neural Networks to Classify EEG Signals Associated to Emotional Stimuli

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Ambient Intelligence for Health (AmIHEALTH 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9456))

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Abstract

This paper presents a strategy to perform an emotional states recognition process by analyzing electroencephalography records; The recognition process were performed by a specific purpose neural network and the experimental criteria for it configuration are presented. Also a novelty electrode discriminant process were applied, which correlates electrodes to Brodmann regions, achieving a data reduction of 29.5 percent. The recognition rates average achieved up to 90.2 percent of recognition rate in the binary case and up to 82.51 percent in a multi-class scheme.

A.R. Aguiñaga—Conacyt and Instituto Tecnológico de Tijuana.

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Notes

  1. 1.

    Information processing (CNS), Support (CNS, NES, ANS), Executive (CNS), Action (SNS), Monitor (CNS).

  2. 2.

    (CNS), central nervous system; (NES), neuro-endocrine system; (ANS), autonomic nervous system; (SNS), somatic nervous system. The organismic subsystems are theoretically postulated functional units or networks.

  3. 3.

    Visual activity are related to occipital region (Brodmann region 17), auditory activity are associated to temporal lobe (region 41), the limbic system as the main responsible to emotional processes are located in the temporal parietal areas and body sensations are located in the temporal and parietal regions [25–27].

  4. 4.

    A neural network with up to fifty units per hidden layer and up to filthy hidden layers.

  5. 5.

    Each performed under a 10-fold cross validation.

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Acknowledgment

To the Instituto Tecnológico de Tijuana and Conacyt for making possible this project.

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Authors and Affiliations

  1. Instituto Tecnológico de Tijuana, Calz Del Tecnológico S/N, Tomas Aquino, 22414, Tijuana, B.C, México

    Adrian Rodriguez Aguiñaga & Miguel Angel Lopez Ramirez

Authors
  1. Adrian Rodriguez Aguiñaga
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  2. Miguel Angel Lopez Ramirez
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Correspondence to Adrian Rodriguez Aguiñaga .

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Editors and Affiliations

  1. Castilla-La Mancha University, CIUDAD REAL, Ciudad Real, Spain

    José Bravo

  2. Castilla-La Mancha University, Ciudad Real, Spain

    Ramón Hervás

  3. Technological University of Panamá, Panama, Panama

    Vladimir Villarreal

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Aguiñaga, A.R., Ramirez, M.A.L. (2015). Low Complexity Neural Networks to Classify EEG Signals Associated to Emotional Stimuli. In: Bravo, J., Hervás, R., Villarreal, V. (eds) Ambient Intelligence for Health. AmIHEALTH 2015. Lecture Notes in Computer Science(), vol 9456. Springer, Cham. https://doi.org/10.1007/978-3-319-26508-7_18

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  • DOI: https://doi.org/10.1007/978-3-319-26508-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26507-0

  • Online ISBN: 978-3-319-26508-7

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