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Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset

Menezes, M. L. R., Samara, A, Galway, L, Sant’Anna, A, Verikas, A, Alonso-Fernandez, F, Wang, H and Bond, R (2017) Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset. Personal and Ubiquitous Computing, 21 (6). pp. 1003-1013. [Journal article]

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URL: https://doi.org/10.1007/s00779-017-1072-7

DOI: 10.1007/s00779-017-1072-7


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 α, β, δ, and θ waves and High Order Crossing of the EEG signal.

Item Type:Journal article
Keywords:Affective Computing, Virtual Environment, EEG, Emotion Recognition, Feature Extraction
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:38512
Deposited By: Dr Leo Galway
Deposited On:08 Sep 2017 11:39
Last Modified:09 Mar 2018 10:07

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