Cargando…
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...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479627/ https://www.ncbi.nlm.nih.gov/pubmed/30978980 http://dx.doi.org/10.3390/s19071738 |
_version_ | 1783413388733317120 |
---|---|
author | Bălan, Oana Moise, Gabriela Moldoveanu, Alin Leordeanu, Marius Moldoveanu, Florica |
author_facet | Bălan, Oana Moise, Gabriela Moldoveanu, Alin Leordeanu, Marius Moldoveanu, Florica |
author_sort | Bălan, Oana |
collection | PubMed |
description | 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—no fear and 1—fear) and the four-level (0—no fear, 1—low fear, 2—medium fear, 3—high fear) 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. |
format | Online Article Text |
id | pubmed-6479627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64796272019-04-29 Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques Bălan, Oana Moise, Gabriela Moldoveanu, Alin Leordeanu, Marius Moldoveanu, Florica Sensors (Basel) Article 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—no fear and 1—fear) and the four-level (0—no fear, 1—low fear, 2—medium fear, 3—high fear) 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. MDPI 2019-04-11 /pmc/articles/PMC6479627/ /pubmed/30978980 http://dx.doi.org/10.3390/s19071738 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bălan, Oana Moise, Gabriela Moldoveanu, Alin Leordeanu, Marius Moldoveanu, Florica Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques |
title | Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques |
title_full | Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques |
title_fullStr | Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques |
title_full_unstemmed | Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques |
title_short | Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques |
title_sort | fear level classification based on emotional dimensions and machine learning techniques |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479627/ https://www.ncbi.nlm.nih.gov/pubmed/30978980 http://dx.doi.org/10.3390/s19071738 |
work_keys_str_mv | AT balanoana fearlevelclassificationbasedonemotionaldimensionsandmachinelearningtechniques AT moisegabriela fearlevelclassificationbasedonemotionaldimensionsandmachinelearningtechniques AT moldoveanualin fearlevelclassificationbasedonemotionaldimensionsandmachinelearningtechniques AT leordeanumarius fearlevelclassificationbasedonemotionaldimensionsandmachinelearningtechniques AT moldoveanuflorica fearlevelclassificationbasedonemotionaldimensionsandmachinelearningtechniques |