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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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 |
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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 |
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