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

Mauro Nascimben*, Thomas Zoëga Ramsøy, Luis Emilio Bruni

*Corresponding author

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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.
Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
Number of pages5
PublisherIEEE
Publication dateOct 2019
Pages445 - 449
Article number8941735
ISBN (Electronic)9781728146171
DOIs
Publication statusPublished - Oct 2019
Event2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) -
Duration: 28 Oct 201930 Oct 2019
https://ieeexplore.ieee.org/xpl/conhome/8936463/proceeding

Conference

Conference2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)
Period28/10/201930/10/2019
Internet address
SeriesInternational Conference on Bioinformatics and Bioengineering
ISSN2471-7819

Keywords

  • EEG
  • emotion recognition
  • human-computer interaction

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