Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques

There has been steady progress in the field of affective computing over the last two decades that has integrated artificial intelligence techniques in the construction of computational models of emotion. Having, as a purpose, the development of a system for treating phobias that would automatically...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-04, Vol.19 (7), p.1738
Main Authors: Bălan, Oana, Moise, Gabriela, Moldoveanu, Alin, Leordeanu, Marius, Moldoveanu, Florica
Format: Article
Language:English
Subjects:
Affective computing
Arousal
Classification
Comparative studies
Discriminant analysis
Electroencephalography
emotional assessment
Emotions
Fear
Fear & phobias
fear classification
Feature recognition
feature selection
Levels
Neural networks
Phobias
Respiration
Support vector machines
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Summary:There has been steady progress in the field of affective computing over the last two decades that has integrated artificial intelligence techniques in the construction of computational models of emotion. Having, as a purpose, the development of a system for treating phobias that would automatically determine fear levels and adapt exposure intensity based on the user's current affective state, we propose a comparative study between various machine and deep learning techniques (four deep neural network models, a stochastic configuration network, Support Vector Machine, Linear Discriminant Analysis, Random Forest and k-Nearest Neighbors), with and without feature selection, for recognizing and classifying fear levels based on the electroencephalogram (EEG) and peripheral data from the DEAP (Database for Emotion Analysis using Physiological signals) database. Fear was considered an emotion eliciting low valence, high arousal and low dominance. By dividing the ratings of valence/arousal/dominance emotion dimensions, we propose two paradigms for fear level estimation-the two-level (0- and 1- ) and the four-level (0- , 1- , 2- , 3- ) paradigms. Although all the methods provide good classification accuracies, the highest F scores have been obtained using the Random Forest Classifier-89.96% and 85.33% for the two-level and four-level fear evaluation modality.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19071738