EEG-based detection of emotional valence towards a reproducible measurement of emotions

Abstract 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 va...

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Autores principales:	Andrea Apicella, Pasquale Arpaia, Giovanna Mastrati, Nicola Moccaldi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/78a3c367698e4cb9952936fa33bd705f
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spelling	oai:doaj.org-article:78a3c367698e4cb9952936fa33bd705f2021-11-08T10:46:29ZEEG-based detection of emotional valence towards a reproducible measurement of emotions10.1038/s41598-021-00812-72045-2322https://doaj.org/article/78a3c367698e4cb9952936fa33bd705f2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00812-7https://doaj.org/toc/2045-2322Abstract 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%.Andrea ApicellaPasquale ArpaiaGiovanna MastratiNicola MoccaldiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andrea Apicella Pasquale Arpaia Giovanna Mastrati Nicola Moccaldi EEG-based detection of emotional valence towards a reproducible measurement of emotions
description	Abstract 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	article
author	Andrea Apicella Pasquale Arpaia Giovanna Mastrati Nicola Moccaldi
author_facet	Andrea Apicella Pasquale Arpaia Giovanna Mastrati Nicola Moccaldi
author_sort	Andrea Apicella
title	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_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_sort	eeg-based detection of emotional valence towards a reproducible measurement of emotions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/78a3c367698e4cb9952936fa33bd705f
work_keys_str_mv	AT andreaapicella eegbaseddetectionofemotionalvalencetowardsareproduciblemeasurementofemotions AT pasqualearpaia eegbaseddetectionofemotionalvalencetowardsareproduciblemeasurementofemotions AT giovannamastrati eegbaseddetectionofemotionalvalencetowardsareproduciblemeasurementofemotions AT nicolamoccaldi eegbaseddetectionofemotionalvalencetowardsareproduciblemeasurementofemotions
_version_	1718442629572591616

EEG-based detection of emotional valence towards a reproducible measurement of emotions

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