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

Roberto Munoz, Rodrigo Olivares, Carla Taramasco, Rodolfo Villarroel, Ricardo Soto, María Francisca Alonso-Sánchez, Erick Merino, Victor Hugo C. de Albuquerque

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish
Pages (from-to)11111-11127
Number of pages17
JournalNeural Computing and Applications
Volume32
Issue number15
DOIs
StatePublished - 1 Aug 2020
Externally publishedYes

Keywords

  • Autonomous bat algorithm
  • Emotion recognition
  • Software architecture
  • Support vector machine

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