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A new EEG software that supports emotion recognition by using an autonomous approach

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Abstract

Human behavior is manly addressed by emotions. One of the most accepted models that represent emotions is known as the circumplex model. This model organizes emotions into points on a bidimensional plane: valence and arousal. Despite the importance of the emotion recognition, there are limited initiatives that seek to classify emotions easily in an uncontrolled environment. In this work, we present the architecture and the design of an extensible software which allows recognizing and classifying emotions by using a low-cost EEG. The proposed software implements an emotion classifier although a support vector machines (SVM) are boosted with an autonomous bio-inspired approach. The contribution was experimentally evaluated by taking a set of well-known validated EEG Databases for Emotion Recognition. Computational experiments show promising results. Using our proposal for EEG emotion classification, we reach an accuracy close to 95%. The results obtained confirm that our approach is able to overcome to a commonly used SVM classifier and that the proposed software can be useful in real environments.

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Notes

  1. https://openbci.com/.

  2. http://neurosky.com/biosensors/eeg-sensor/biosensors/.

  3. https://github.com/leikelen-team/Leikelen/releases/tag/v1.2.0.

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Acknowledgements

Carla Taramasco has been supported by CORFO—CENS 16CTTS-66390 through the National Center on Health Information Systems. This work is also supported by the National Commission for Scientific and Technological Research (CONICYT) through the Program STIC-AMSUD 17STIC- 03: “MONITORing for ehealth,” FONDEF ID16I10449 “Sistema inteligente para la gestión y análisis de la dotación de camas en la red asistencial del sector público”, and MEC80170097 “Red de colaboración científica entre universidades nacionales e internacionales para la estructuración del doctorado y magister en informática médica en la Universidad de Valparaíso.” Ricardo Soto is supported by Grant CONICYT/FONDECYT/REGULAR/1160455. María Francisca Alonso-Sńchez is supported by CONICYT/FONDECYT/INICIACION/11160212. Victor Hugo C. de Albuquerque appreciates the received support from the Brazilian National Council for Research and Development (CNPq, Grant #304315/2017-6).

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Authors and Affiliations

  1. Escuela de Ingeniería Civil Informática, Universidad de Valparaíso, Valparaíso, Chile

    Roberto Munoz, Rodrigo Olivares, Carla Taramasco & Erick Merino

  2. Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile

    Roberto Munoz & Carla Taramasco

  3. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Rodolfo Villarroel & Ricardo Soto

  4. Escuela de Fonoaudiología, Facultad de Salud, Universidad Santo Tomás, Viña del Mar, Chile

    María Francisca Alonso-Sánchez

  5. Graduate Program in Applied Informatics, Laboratory of Bioinformatics, University of Fortaleza, Fortaleza, CE, Brazil

    Victor Hugo C. de Albuquerque

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  1. Roberto Munoz
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Correspondence to Rodrigo Olivares.

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The data generated to support the findings of this study have been deposited in the Figshare repository (https://figshare.com/s/1a80d472266b39ed2723).

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Munoz, R., Olivares, R., Taramasco, C. et al. A new EEG software that supports emotion recognition by using an autonomous approach. Neural Comput & Applic 32, 11111–11127 (2020). https://doi.org/10.1007/s00521-018-3925-z

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