Cargando…
Identifying relevant asymmetry features of EEG for emotion processing
The left and right hemispheres of the brain process emotion differently. Neuroscientists have proposed two models to explain this difference. The first model states that the right hemisphere is dominant over the left to process all emotions. In contrast, the second model states that the left hemisph...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469865/ https://www.ncbi.nlm.nih.gov/pubmed/37663334 http://dx.doi.org/10.3389/fpsyg.2023.1217178 |
_version_ | 1785099542075539456 |
---|---|
author | Mouri, Fatima Islam Valderrama, Camilo E. Camorlinga, Sergio G. |
author_facet | Mouri, Fatima Islam Valderrama, Camilo E. Camorlinga, Sergio G. |
author_sort | Mouri, Fatima Islam |
collection | PubMed |
description | The left and right hemispheres of the brain process emotion differently. Neuroscientists have proposed two models to explain this difference. The first model states that the right hemisphere is dominant over the left to process all emotions. In contrast, the second model states that the left hemisphere processes positive emotions, whereas the right hemisphere processes negative emotions. Previous studies have used these asymmetry models to enhance the classification of emotions in machine learning models. However, little research has been conducted to explore how machine learning models can help identify associations between hemisphere asymmetries and emotion processing. To address this gap, we conducted two experiments using a subject-independent approach to explore how the asymmetry of the brain hemispheres is involved in processing happiness, sadness, fear, and neutral emotions. We analyzed electroencephalogram (EEG) signals from 15 subjects collected while they watched video clips evoking these four emotions. We derived asymmetry features from the recorded EEG signals by calculating the log ratio between the relative energy of symmetrical left and right nodes. Using the asymmetry features, we trained four binary logistic regressions, one for each emotion, to identify which features were more relevant to the predictions. The average AUC-ROC across the 15 subjects was 56.2, 54.6, 51.6, and 58.4% for neutral, sad, fear, and happy, respectively. We validated these results with an independent dataset, achieving comparable AUC-ROC values. Our results showed that brain lateralization was observed primarily in the alpha frequency bands, whereas for the other frequency bands, both hemispheres were involved in emotion processing. Furthermore, the logistic regression analysis indicated that the gamma and alpha bands were the most relevant for predicting emotional states, particularly for the lateral frontal, parietal, and temporal EEG pairs, such as FT7-FT8, T7-T8, and TP7-TP8. These findings provide valuable insights into which brain areas and frequency bands need to be considered when developing predictive models for emotion recognition. |
format | Online Article Text |
id | pubmed-10469865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104698652023-09-01 Identifying relevant asymmetry features of EEG for emotion processing Mouri, Fatima Islam Valderrama, Camilo E. Camorlinga, Sergio G. Front Psychol Psychology The left and right hemispheres of the brain process emotion differently. Neuroscientists have proposed two models to explain this difference. The first model states that the right hemisphere is dominant over the left to process all emotions. In contrast, the second model states that the left hemisphere processes positive emotions, whereas the right hemisphere processes negative emotions. Previous studies have used these asymmetry models to enhance the classification of emotions in machine learning models. However, little research has been conducted to explore how machine learning models can help identify associations between hemisphere asymmetries and emotion processing. To address this gap, we conducted two experiments using a subject-independent approach to explore how the asymmetry of the brain hemispheres is involved in processing happiness, sadness, fear, and neutral emotions. We analyzed electroencephalogram (EEG) signals from 15 subjects collected while they watched video clips evoking these four emotions. We derived asymmetry features from the recorded EEG signals by calculating the log ratio between the relative energy of symmetrical left and right nodes. Using the asymmetry features, we trained four binary logistic regressions, one for each emotion, to identify which features were more relevant to the predictions. The average AUC-ROC across the 15 subjects was 56.2, 54.6, 51.6, and 58.4% for neutral, sad, fear, and happy, respectively. We validated these results with an independent dataset, achieving comparable AUC-ROC values. Our results showed that brain lateralization was observed primarily in the alpha frequency bands, whereas for the other frequency bands, both hemispheres were involved in emotion processing. Furthermore, the logistic regression analysis indicated that the gamma and alpha bands were the most relevant for predicting emotional states, particularly for the lateral frontal, parietal, and temporal EEG pairs, such as FT7-FT8, T7-T8, and TP7-TP8. These findings provide valuable insights into which brain areas and frequency bands need to be considered when developing predictive models for emotion recognition. Frontiers Media S.A. 2023-08-17 /pmc/articles/PMC10469865/ /pubmed/37663334 http://dx.doi.org/10.3389/fpsyg.2023.1217178 Text en Copyright © 2023 Mouri, Valderrama and Camorlinga. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Mouri, Fatima Islam Valderrama, Camilo E. Camorlinga, Sergio G. Identifying relevant asymmetry features of EEG for emotion processing |
title | Identifying relevant asymmetry features of EEG for emotion processing |
title_full | Identifying relevant asymmetry features of EEG for emotion processing |
title_fullStr | Identifying relevant asymmetry features of EEG for emotion processing |
title_full_unstemmed | Identifying relevant asymmetry features of EEG for emotion processing |
title_short | Identifying relevant asymmetry features of EEG for emotion processing |
title_sort | identifying relevant asymmetry features of eeg for emotion processing |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469865/ https://www.ncbi.nlm.nih.gov/pubmed/37663334 http://dx.doi.org/10.3389/fpsyg.2023.1217178 |
work_keys_str_mv | AT mourifatimaislam identifyingrelevantasymmetryfeaturesofeegforemotionprocessing AT valderramacamiloe identifyingrelevantasymmetryfeaturesofeegforemotionprocessing AT camorlingasergiog identifyingrelevantasymmetryfeaturesofeegforemotionprocessing |