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Computational Intelligence and Neuroscience
Volume 2018, Article ID 3050214, 21 pages
Research Article

Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition

1Escuela de Ingeniería Civil Informática, Universidad de Valparaíso, Valparaíso, Chile
2Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
3Instituto Federal de Educação, Ciência e Tecnologia de São Paulo, Brazil
4Centro de Investigación del Desarrollo en Cognición y Lenguaje, Universidad de Valparaíso, Valparaíso, Chile

Correspondence should be addressed to Roberto Munoz;

Received 19 February 2018; Revised 18 April 2018; Accepted 8 May 2018; Published 11 June 2018

Academic Editor: Victor H. C. de Albuquerque

Copyright © 2018 Roberto Munoz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Emotions are a critical aspect of human behavior. One widely used technique for research in emotion measurement is based on the use of EEG signals. In general terms, the first step of signal processing is the elimination of noise, which can be done in manual or automatic terms. The next step is determining the feature vector using, for example, entropy calculation and its variations to generate a classification model. It is possible to use this approach to classify theoretical models such as the Circumplex model. This model proposes that emotions are distributed in a two-dimensional circular space. However, methods to determine the feature vector are highly susceptible to noise that may exist in the signal. In this article, a new method to adjust the classifier is proposed using metaheuristics based on the black hole algorithm. The method is aimed at obtaining results similar to those obtained with manual noise elimination methods. In order to evaluate the proposed method, the MAHNOB HCI Tagging Database was used. Results show that using the black hole algorithm to optimize the feature vector of the Support Vector Machine we obtained an accuracy of 92.56% over 30 executions.