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EEG-based detection of emotional valence towards a reproducible measurement of emotions
A methodological contribution to a reproducible Measurement of Emotions for an EEG-based system is proposed. Emotional Valence detection is the suggested use case. Valence detection occurs along the interval scale theorized by the Circumplex Model of emotions. The binary choice, positive valence vs...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8566577/ https://www.ncbi.nlm.nih.gov/pubmed/34732756 http://dx.doi.org/10.1038/s41598-021-00812-7 |
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author | Apicella, Andrea Arpaia, Pasquale Mastrati, Giovanna Moccaldi, Nicola |
author_facet | Apicella, Andrea Arpaia, Pasquale Mastrati, Giovanna Moccaldi, Nicola |
author_sort | Apicella, Andrea |
collection | PubMed |
description | A methodological contribution to a reproducible Measurement of Emotions for an EEG-based system is proposed. Emotional Valence detection is the suggested use case. Valence detection occurs along the interval scale theorized by the Circumplex Model of emotions. The binary choice, positive valence vs negative valence, represents a first step towards the adoption of a metric scale with a finer resolution. EEG signals were acquired through a 8-channel dry electrode cap. An implicit-more controlled EEG paradigm was employed to elicit emotional valence through the passive view of standardized visual stimuli (i.e., Oasis dataset) in 25 volunteers without depressive disorders. Results from the Self Assessment Manikin questionnaire confirmed the compatibility of the experimental sample with that of Oasis. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e., Hemispheric Asymmetry Theories), and (ii) automated (i.e., a pipeline of a custom 12-band Filter Bank and Common Spatial Pattern). An average within-subject accuracy of 96.1 %, was obtained by a shallow Artificial Neural Network, while k-Nearest Neighbors allowed to obtain a cross-subject accuracy equal to 80.2%. |
format | Online Article Text |
id | pubmed-8566577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85665772021-11-05 EEG-based detection of emotional valence towards a reproducible measurement of emotions Apicella, Andrea Arpaia, Pasquale Mastrati, Giovanna Moccaldi, Nicola Sci Rep Article A methodological contribution to a reproducible Measurement of Emotions for an EEG-based system is proposed. Emotional Valence detection is the suggested use case. Valence detection occurs along the interval scale theorized by the Circumplex Model of emotions. The binary choice, positive valence vs negative valence, represents a first step towards the adoption of a metric scale with a finer resolution. EEG signals were acquired through a 8-channel dry electrode cap. An implicit-more controlled EEG paradigm was employed to elicit emotional valence through the passive view of standardized visual stimuli (i.e., Oasis dataset) in 25 volunteers without depressive disorders. Results from the Self Assessment Manikin questionnaire confirmed the compatibility of the experimental sample with that of Oasis. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e., Hemispheric Asymmetry Theories), and (ii) automated (i.e., a pipeline of a custom 12-band Filter Bank and Common Spatial Pattern). An average within-subject accuracy of 96.1 %, was obtained by a shallow Artificial Neural Network, while k-Nearest Neighbors allowed to obtain a cross-subject accuracy equal to 80.2%. Nature Publishing Group UK 2021-11-03 /pmc/articles/PMC8566577/ /pubmed/34732756 http://dx.doi.org/10.1038/s41598-021-00812-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Apicella, Andrea Arpaia, Pasquale Mastrati, Giovanna Moccaldi, Nicola EEG-based detection of emotional valence towards a reproducible measurement of emotions |
title | EEG-based detection of emotional valence towards a reproducible measurement of emotions |
title_full | EEG-based detection of emotional valence towards a reproducible measurement of emotions |
title_fullStr | EEG-based detection of emotional valence towards a reproducible measurement of emotions |
title_full_unstemmed | EEG-based detection of emotional valence towards a reproducible measurement of emotions |
title_short | EEG-based detection of emotional valence towards a reproducible measurement of emotions |
title_sort | eeg-based detection of emotional valence towards a reproducible measurement of emotions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8566577/ https://www.ncbi.nlm.nih.gov/pubmed/34732756 http://dx.doi.org/10.1038/s41598-021-00812-7 |
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