Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition

Roberto Munoz, Rodrigo Olivares, Carla Taramasco, RODOLFO HUMBERTO VILLARROEL ACEVEDO, RICARDO JAVIER SOTO DE GIORGIS, Thiago S. Barcelos, Erick Merino, María Francisca Alonso-Sánchez

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

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.

Original languageEnglish
Article number3050214
JournalComputational Intelligence and Neuroscience
Volume2018
DOIs
StatePublished - 1 Jan 2018

Fingerprint Dive into the research topics of 'Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition'. Together they form a unique fingerprint.

Cite this