TY - JOUR
T1 - A new EEG software that supports emotion recognition by using an autonomous approach
AU - Munoz, Roberto
AU - Olivares, Rodrigo
AU - Taramasco, Carla
AU - Villarroel, Rodolfo
AU - Soto, Ricardo
AU - Alonso-Sánchez, María Francisca
AU - Merino, Erick
AU - de Albuquerque, Victor Hugo C.
N1 - Publisher Copyright:
© 2018, Springer-Verlag London Ltd., part of Springer Nature.
PY - 2020/8/1
Y1 - 2020/8/1
N2 - 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.
AB - 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.
KW - Autonomous bat algorithm
KW - Emotion recognition
KW - Software architecture
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85058059478&partnerID=8YFLogxK
U2 - 10.1007/s00521-018-3925-z
DO - 10.1007/s00521-018-3925-z
M3 - Article
AN - SCOPUS:85058059478
SN - 0941-0643
VL - 32
SP - 11111
EP - 11127
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 15
ER -