Cargando…

Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques

Many scientific researchers’ study focuses on enhancing automated systems to identify emotions and thus relies on brain signals. This study focuses on how brain wave signals can be used to classify many emotional states of humans. Electroencephalography (EEG)-based affective computing predominantly...

Descripción completa

Detalles Bibliográficos
Autores principales: ArulDass, Stephen Dass, Jayagopal, Prabhu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777297/
https://www.ncbi.nlm.nih.gov/pubmed/36553197
http://dx.doi.org/10.3390/diagnostics12123188
_version_ 1784856068768137216
author ArulDass, Stephen Dass
Jayagopal, Prabhu
author_facet ArulDass, Stephen Dass
Jayagopal, Prabhu
author_sort ArulDass, Stephen Dass
collection PubMed
description Many scientific researchers’ study focuses on enhancing automated systems to identify emotions and thus relies on brain signals. This study focuses on how brain wave signals can be used to classify many emotional states of humans. Electroencephalography (EEG)-based affective computing predominantly focuses on emotion classification based on facial expression, speech recognition, and text-based recognition through multimodality stimuli. The proposed work aims to implement a methodology to identify and codify discrete complex emotions such as pleasure and grief in a rare psychological disorder known as alexithymia. This type of disorder is highly elicited in unstable, fragile countries such as South Sudan, Lebanon, and Mauritius. These countries are continuously affected by civil wars and disaster and politically unstable, leading to a very poor economy and education system. This study focuses on an adolescent age group dataset by recording physiological data when emotion is exhibited in a multimodal virtual environment. We decocted time frequency analysis and amplitude time series correlates including frontal alpha symmetry using a complex Morlet wavelet. For data visualization, we used the UMAP technique to obtain a clear district view of emotions. We performed 5-fold cross validation along with 1 s window subjective classification on the dataset. We opted for traditional machine learning techniques to identify complex emotion labeling.
format Online
Article
Text
id pubmed-9777297
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-97772972022-12-23 Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques ArulDass, Stephen Dass Jayagopal, Prabhu Diagnostics (Basel) Article Many scientific researchers’ study focuses on enhancing automated systems to identify emotions and thus relies on brain signals. This study focuses on how brain wave signals can be used to classify many emotional states of humans. Electroencephalography (EEG)-based affective computing predominantly focuses on emotion classification based on facial expression, speech recognition, and text-based recognition through multimodality stimuli. The proposed work aims to implement a methodology to identify and codify discrete complex emotions such as pleasure and grief in a rare psychological disorder known as alexithymia. This type of disorder is highly elicited in unstable, fragile countries such as South Sudan, Lebanon, and Mauritius. These countries are continuously affected by civil wars and disaster and politically unstable, leading to a very poor economy and education system. This study focuses on an adolescent age group dataset by recording physiological data when emotion is exhibited in a multimodal virtual environment. We decocted time frequency analysis and amplitude time series correlates including frontal alpha symmetry using a complex Morlet wavelet. For data visualization, we used the UMAP technique to obtain a clear district view of emotions. We performed 5-fold cross validation along with 1 s window subjective classification on the dataset. We opted for traditional machine learning techniques to identify complex emotion labeling. MDPI 2022-12-16 /pmc/articles/PMC9777297/ /pubmed/36553197 http://dx.doi.org/10.3390/diagnostics12123188 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
ArulDass, Stephen Dass
Jayagopal, Prabhu
Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques
title Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques
title_full Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques
title_fullStr Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques
title_full_unstemmed Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques
title_short Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques
title_sort identifying complex emotions in alexithymia affected adolescents using machine learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777297/
https://www.ncbi.nlm.nih.gov/pubmed/36553197
http://dx.doi.org/10.3390/diagnostics12123188
work_keys_str_mv AT aruldassstephendass identifyingcomplexemotionsinalexithymiaaffectedadolescentsusingmachinelearningtechniques
AT jayagopalprabhu identifyingcomplexemotionsinalexithymiaaffectedadolescentsusingmachinelearningtechniques