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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 language | English |
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Title of host publication | Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019 |
Number of pages | 5 |
Publisher | IEEE |
Publication date | Oct 2019 |
Pages | 445 - 449 |
Article number | 8941735 |
ISBN (Electronic) | 9781728146171 |
DOIs | |
Publication status | Published - Oct 2019 |
Event | 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) - Duration: 28 Oct 2019 → 30 Oct 2019 https://ieeexplore.ieee.org/xpl/conhome/8936463/proceeding |
Conference
Conference | 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) |
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Period | 28/10/2019 → 30/10/2019 |
Internet address |
Series | International Conference on Bioinformatics and Bioengineering |
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ISSN | 2471-7819 |
Keywords
- EEG
- emotion recognition
- human-computer interaction
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Dive into the research topics of 'User-independent classification of emotions in a mixed arousal-valence model'. Together they form a unique fingerprint.Projects
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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 → …
Project: Research