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Stylized faces enhance ERP features used for the detection of emotional responses
For their ease of accessibility and low cost, current Brain-Computer Interfaces (BCI) used to detect subjective emotional and affective states rely largely on electroencephalographic (EEG) signals. Public datasets are available for researchers to design models for affect detection from EEG. However,...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174306/ https://www.ncbi.nlm.nih.gov/pubmed/37180552 http://dx.doi.org/10.3389/fnhum.2023.1160800 |
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author | Barradas-Chacón, Luis Alberto Brunner, Clemens Wriessnegger, Selina C. |
author_facet | Barradas-Chacón, Luis Alberto Brunner, Clemens Wriessnegger, Selina C. |
author_sort | Barradas-Chacón, Luis Alberto |
collection | PubMed |
description | For their ease of accessibility and low cost, current Brain-Computer Interfaces (BCI) used to detect subjective emotional and affective states rely largely on electroencephalographic (EEG) signals. Public datasets are available for researchers to design models for affect detection from EEG. However, few designs focus on optimally exploiting the nature of the stimulus elicitation to improve accuracy. The RSVP protocol is used in this experiment to present human faces of emotion to 28 participants while EEG was measured. We found that artificially enhanced human faces with exaggerated, cartoonish visual features significantly improve some commonly used neural correlates of emotion as measured by event-related potentials (ERPs). These images elicit an enhanced N170 component, well known to relate to the facial visual encoding process. Our findings suggest that the study of emotion elicitation could exploit consistent, high detail, AI generated stimuli transformations to study the characteristics of electrical brain activity related to visual affective stimuli. Furthermore, this specific result might be useful in the context of affective BCI design, where a higher accuracy in affect decoding from EEG can improve the experience of a user. |
format | Online Article Text |
id | pubmed-10174306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101743062023-05-12 Stylized faces enhance ERP features used for the detection of emotional responses Barradas-Chacón, Luis Alberto Brunner, Clemens Wriessnegger, Selina C. Front Hum Neurosci Neuroscience For their ease of accessibility and low cost, current Brain-Computer Interfaces (BCI) used to detect subjective emotional and affective states rely largely on electroencephalographic (EEG) signals. Public datasets are available for researchers to design models for affect detection from EEG. However, few designs focus on optimally exploiting the nature of the stimulus elicitation to improve accuracy. The RSVP protocol is used in this experiment to present human faces of emotion to 28 participants while EEG was measured. We found that artificially enhanced human faces with exaggerated, cartoonish visual features significantly improve some commonly used neural correlates of emotion as measured by event-related potentials (ERPs). These images elicit an enhanced N170 component, well known to relate to the facial visual encoding process. Our findings suggest that the study of emotion elicitation could exploit consistent, high detail, AI generated stimuli transformations to study the characteristics of electrical brain activity related to visual affective stimuli. Furthermore, this specific result might be useful in the context of affective BCI design, where a higher accuracy in affect decoding from EEG can improve the experience of a user. Frontiers Media S.A. 2023-04-27 /pmc/articles/PMC10174306/ /pubmed/37180552 http://dx.doi.org/10.3389/fnhum.2023.1160800 Text en Copyright © 2023 Barradas-Chacón, Brunner and Wriessnegger. 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 | Neuroscience Barradas-Chacón, Luis Alberto Brunner, Clemens Wriessnegger, Selina C. Stylized faces enhance ERP features used for the detection of emotional responses |
title | Stylized faces enhance ERP features used for the detection of emotional responses |
title_full | Stylized faces enhance ERP features used for the detection of emotional responses |
title_fullStr | Stylized faces enhance ERP features used for the detection of emotional responses |
title_full_unstemmed | Stylized faces enhance ERP features used for the detection of emotional responses |
title_short | Stylized faces enhance ERP features used for the detection of emotional responses |
title_sort | stylized faces enhance erp features used for the detection of emotional responses |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174306/ https://www.ncbi.nlm.nih.gov/pubmed/37180552 http://dx.doi.org/10.3389/fnhum.2023.1160800 |
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