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

ABSTRACT: 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
\'z, \b{eta}, \'z, and \'z\'z waves and High Order Crossing of the EEG signal.
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
\'z, \b{eta}, \'z, and \'z\'z waves and High Order Crossing of the EEG signal.
Towards emotion recognition for virtual environments: an evaluation of EEG features on benchmark dataset
http://arxiv.org/abs/2210.13876v1