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Conditional Entropy Estimates for Distress Detection with EEG Signals

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Natural and Artificial Computation for Biomedicine and Neuroscience (IWINAC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10337))

Abstract

Recently, distress has become a major problem in most advanced societies because of its negative side effects in physical and mental health. In this sense, the assessment of different physiological signals such as electroencephalogram (EEG) provides new insights about the body’s reaction against distressful stimuli. Moreover, the non-linear and dynamic behaviour of the brain suggests the application of non-linear methodologies for EEG analysis. In this work, a symbolic technique called conditional entropy was applied for the assessment of 279 32-EEG channel segments of calm and distress emotional states. Results of all EEG electrodes were combined in a simple decision tree classifier, reporting a discriminatory power above 70%. Furthermore, a decreasing tendency of irregularity when changing from calm to distress conditions was observed for all EEG channels. The simplicity of this classification model allows an easy interpretation of the results, together with a possible implementation of the algorithm in a real-time monitoring system.

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Acknowledgements

This work was partially supported by Spanish Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación (AEI)/European Regional Development Fund under HA-SYMBIOSIS (TIN2015-72931-EXP), Vi-SMARt (TIN2016-79100-R) and EmoBioFeedback (DPI2016-80894-R) grants.

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Authors and Affiliations

  1. Instituto de Tecnologías Audiovisuales, Universidad de Castilla-La Mancha, 16071, Cuenca, Spain

    Beatriz García-Martínez, Arturo Martínez-Rodrigo & Raúl Alcaraz

  2. Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071, Albacete, Spain

    Antonio Fernández-Caballero & Pascual González

Authors
  1. Beatriz García-Martínez
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  2. Arturo Martínez-Rodrigo
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  3. Antonio Fernández-Caballero
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  4. Pascual González
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  5. Raúl Alcaraz
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Corresponding author

Correspondence to Beatriz García-Martínez .

Editor information

Editors and Affiliations

  1. Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Departamento de Inteligencia Articial, Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Departamento de Inteligencia Articial, Universidad Nacional de Educación a Distancia, Madrid, Spain

    Félix de la Paz López

  4. Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain

    Javier Toledo Moreo

  5. The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

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García-Martínez, B., Martínez-Rodrigo, A., Fernández-Caballero, A., González, P., Alcaraz, R. (2017). Conditional Entropy Estimates for Distress Detection with EEG Signals. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_19

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  • DOI: https://doi.org/10.1007/978-3-319-59740-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59739-3

  • Online ISBN: 978-3-319-59740-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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