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Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics

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Abstract

Recently, the recognition of emotions with electroencephalographic (EEG) signals has received increasing attention. Furthermore, the nonstationarity of brain has intensified the application of nonlinear methods. Nonetheless, metrics like quadratic sample entropy (QSE), amplitude-aware permutation entropy (AAPE) and permutation min-entropy (PME) have never been applied to discern between more than two emotions. Therefore, this study computes for the first time QSE, AAPE and PME for recognition of four groups of emotions. After preprocessing the EEG recordings, the three entropy metrics were computed. Then, a tenfold classification approach based on a sequential forward selection scheme and a support vector machine classifier was implemented. This procedure was applied in a multi-class scheme including the four groups of study simultaneously, and in a binary-class approach for discerning emotions two by two, regarding their levels of arousal and valence. For both schemes, QSE+AAPE and QSE+PME were combined. In both multi-class and binary-class schemes, the best results were obtained in frontal and parietal brain areas. Furthermore, in most of the cases channels from QSE and AAPE/PME were selected in the classification models, thus highlighting the complementarity between those different types of entropy indices and achieving global accuracy results higher than 90% in multi-class and binary-class schemes. The combination of regularity- and predictability-based entropy indices denoted a high degree of complementarity between those nonlinear methods. Finally, the relevance of frontal and parietal areas for recognition of emotions has revealed the essential role of those brain regions in emotional processes.

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Acknowledgements

This work was partially supported by Spanish Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (AEI) / European Regional Development Fund (FEDER, UE) under DPI2016-440 80894-R, PID2019-106084RB-I00 and 2018/11744 grants, by Castilla-La Mancha Regional Government / FEDER, UE under SBPLY/17/180501/000192 grant, and by Biomedical Research Networking Centre in Mental Health (CIBERSAM) of the Instituto de Salud Carlos III. Beatriz García-Martínez holds FPU16/03740 scholarship from Spanish Ministerio de Educación y Formación Profesional. Luciano Zunino acknowledges the financial support from Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina.

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Authors and Affiliations

  1. Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla-La Mancha, Albacete, Spain

    Beatriz García-Martínez & Antonio Fernández-Caballero

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

    Beatriz García-Martínez & Antonio Fernández-Caballero

  3. CIBERSAM (Biomedical Research Networking Centre in Mental Health), Madrid, Spain

    Antonio Fernández-Caballero

  4. Centro de Investigaciones Ópticas (CONICET La Plata-CIC), C.C. 3, 1897, Gonnet, Argentina

    Luciano Zunino

  5. Departamento de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), 1900, La Plata, Argentina

    Luciano Zunino

  6. Departamento de Sistemas Informáticos, Escuela Politécnica de Cuenca, Universidad de Castilla-La Mancha, Cuenca, Spain

    Arturo Martínez-Rodrigo

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

    Arturo Martínez-Rodrigo

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  1. Beatriz García-Martínez
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  2. Antonio Fernández-Caballero
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  3. Luciano Zunino
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  4. Arturo Martínez-Rodrigo
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Correspondence to Arturo Martínez-Rodrigo.

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García-Martínez, B., Fernández-Caballero, A., Zunino, L. et al. Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics. Cogn Comput 13, 403–417 (2021). https://doi.org/10.1007/s12559-020-09789-3

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  • DOI: https://doi.org/10.1007/s12559-020-09789-3

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