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 proceedingKonferenceartikel i proceedingForskningpeer review

Abstrakt

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.
OriginalsprogEngelsk
TitelProceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
Antal sider5
ForlagIEEE
Publikationsdatookt. 2019
Sider445 - 449
Artikelnummer8941735
ISBN (Elektronisk)9781728146171
DOI
StatusUdgivet - okt. 2019
Begivenhed2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) -
Varighed: 28 okt. 201930 okt. 2019
https://ieeexplore.ieee.org/xpl/conhome/8936463/proceeding

Konference

Konference2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)
Periode28/10/201930/10/2019
Internetadresse
NavnInternational Conference on Bioinformatics and Bioengineering
ISSN2471-7819

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