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Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3495-2961
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2017 (English)In: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 21, no 6, p. 1003-1013Article in journal (Refereed) Published
Abstract [en]

One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user’s emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell’s Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the and waves and High Order Crossing of the EEG signal. © 2017, The Author(s).

Place, publisher, year, edition, pages
London: Springer London, 2017. Vol. 21, no 6, p. 1003-1013
Keywords [en]
Classification (of information), Decision trees, Electroencephalography, Feature extraction, Speech recognition, Virtual reality, Affective Computing, Affective state, Benchmark datasets, Circumplex models, Classification accuracy, Electroencephalogram signals, Emotion recognition, Interaction experiences, Behavioral research
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-36499DOI: 10.1007/s00779-017-1072-7ISI: 000416170900005Scopus ID: 2-s2.0-85027845103OAI: oai:DiVA.org:hh-36499DiVA, id: diva2:1218277
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cited By 1

Available from: 2018-06-14 Created: 2018-06-14 Last updated: 2018-06-14Bibliographically approved

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Menezes, Maria Luiza RecenaPinheiro Sant'Anna, AnitaVerikas, AntanasAlonso-Fernandez, Fernando

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Menezes, Maria Luiza RecenaPinheiro Sant'Anna, AnitaVerikas, AntanasAlonso-Fernandez, Fernando
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Halmstad Embedded and Intelligent Systems Research (EIS)CAISR - Center for Applied Intelligent Systems Research
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Personal and Ubiquitous Computing
Signal Processing

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