User-independent classification of emotions in a mixed arousal-valence model

Mauro Nascimben; Thomas Zoëga  Ramsøy; Luis Emilio Bruni

doi:10.1109/BIBE.2019.00086

User-independent classification of emotions in a mixed arousal-valence model

Mauro Nascimben^*, Thomas Zoëga Ramsøy, Luis Emilio Bruni

^*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

1 Citationer (Scopus)

Abstract

In this work we classified EEG features connected with emotions elicited by musical videos. To detect emotions, we used a user-independent approach with data coming from multiple participants in order to test the "peak-end rule". Participant's video ratings were processed to create a mixed valence-arousal labelling. Input features were refined using a combination of feature ranking and data reduction based on intrinsic dimensionality search. Compared to previous literature, our results show that the proposed mixed arousal-valence classification is compatible with previous works applying a distinct arousal or valence classification.

Originalsprog	Engelsk
Titel	Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
Antal sider	5
Forlag	IEEE
Publikationsdato	okt. 2019
Sider	445 - 449
Artikelnummer	8941735
ISBN (Elektronisk)	9781728146171
DOI	https://doi.org/10.1109/BIBE.2019.00086
Status	Udgivet - okt. 2019
Begivenhed	2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) - Varighed: 28 okt. 2019 → 30 okt. 2019 https://ieeexplore.ieee.org/xpl/conhome/8936463/proceeding

Konference

Konference	2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)
Periode	28/10/2019 → 30/10/2019
Internetadresse	https://ieeexplore.ieee.org/xpl/conhome/8936463/proceeding

Navn	International Conference on Bioinformatics and Bioengineering
ISSN	2471-7819

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10.1109/BIBE.2019.00086

https://zenodo.org/record/4227854/files/IEEEBIBE2019Emot.pdf

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ViZARTS: Visualization and Adaptive Real-Time Storytelling - for Film, Animation and Games
Fog, H. S., Larsen, B. A., Reng, L., Bruni, L. E., Selvig, D. R., Mødekjær, C., Hussain, A., Pasalic, A., Thomsen, M. R., Ditlevsen, D. H., Gymoese, T. & Risvang, A. K.
01/10/2019 → …
Projekter: Projekt › Forskning

Citationsformater

@inproceedings{ae00249ffa85460f9c13cf251d0719be,

title = "User-independent classification of emotions in a mixed arousal-valence model",

abstract = "In this work we classified EEG features connected with emotions elicited by musical videos. To detect emotions, we used a user-independent approach with data coming from multiple participants in order to test the {"}peak-end rule{"}. Participant's video ratings were processed to create a mixed valence-arousal labelling. Input features were refined using a combination of feature ranking and data reduction based on intrinsic dimensionality search. Compared to previous literature, our results show that the proposed mixed arousal-valence classification is compatible with previous works applying a distinct arousal or valence classification.",

keywords = "EEG, emotion recognition, human-computer interaction",

author = "Mauro Nascimben and Rams{\o}y, {Thomas Zo{\"e}ga} and Bruni, {Luis Emilio}",

year = "2019",

month = oct,

doi = "10.1109/BIBE.2019.00086",

language = "English",

series = "International Conference on Bioinformatics and Bioengineering",

pages = "445 -- 449",

booktitle = "Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019",

publisher = "IEEE",

address = "United States",

note = "2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) ; Conference date: 28-10-2019 Through 30-10-2019",

url = "https://ieeexplore.ieee.org/xpl/conhome/8936463/proceeding",

}

Nascimben, M, Ramsøy, TZ & Bruni, LE 2019, User-independent classification of emotions in a mixed arousal-valence model. i Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019., 8941735, IEEE, International Conference on Bioinformatics and Bioengineering, s. 445 - 449, 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), 28/10/2019. https://doi.org/10.1109/BIBE.2019.00086

User-independent classification of emotions in a mixed arousal-valence model. / Nascimben, Mauro; Ramsøy, Thomas Zoëga ; Bruni, Luis Emilio.
Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019. IEEE, 2019. s. 445 - 449 8941735 (International Conference on Bioinformatics and Bioengineering).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - User-independent classification of emotions in a mixed arousal-valence model

AU - Nascimben, Mauro

AU - Ramsøy, Thomas Zoëga

AU - Bruni, Luis Emilio

PY - 2019/10

Y1 - 2019/10

N2 - In this work we classified EEG features connected with emotions elicited by musical videos. To detect emotions, we used a user-independent approach with data coming from multiple participants in order to test the "peak-end rule". Participant's video ratings were processed to create a mixed valence-arousal labelling. Input features were refined using a combination of feature ranking and data reduction based on intrinsic dimensionality search. Compared to previous literature, our results show that the proposed mixed arousal-valence classification is compatible with previous works applying a distinct arousal or valence classification.

AB - In this work we classified EEG features connected with emotions elicited by musical videos. To detect emotions, we used a user-independent approach with data coming from multiple participants in order to test the "peak-end rule". Participant's video ratings were processed to create a mixed valence-arousal labelling. Input features were refined using a combination of feature ranking and data reduction based on intrinsic dimensionality search. Compared to previous literature, our results show that the proposed mixed arousal-valence classification is compatible with previous works applying a distinct arousal or valence classification.

KW - EEG

KW - emotion recognition

KW - human-computer interaction

UR - http://www.scopus.com/inward/record.url?scp=85078071003&partnerID=8YFLogxK

U2 - 10.1109/BIBE.2019.00086

DO - 10.1109/BIBE.2019.00086

M3 - Article in proceeding

T3 - International Conference on Bioinformatics and Bioengineering

SP - 445

EP - 449

BT - Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019

PB - IEEE

T2 - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)

Y2 - 28 October 2019 through 30 October 2019

ER -

User-independent classification of emotions in a mixed arousal-valence model

Abstract

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ViZARTS: Visualization and Adaptive Real-Time Storytelling - for Film, Animation and Games

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