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One of the challenges in virtual environments is the difficulty users have ininteracting with these increasingly complex systems. Ultimately, endowingmachines with the ability to perceive users emotions will enable a moreintuitive and reliable interaction. Consequently, using theelectroencephalogram as a bio-signal sensor, the affective state of a user canbe modelled and subsequently utilised in order to achieve a system that canrecognise and react to the user's emotions. This paper investigates featuresextracted from electroencephalogram signals for the purpose of affective statemodelling based on Russell's Circumplex Model. Investigations are presentedthat aim to provide the foundation for future work in modelling user affect toenhance interaction experience in virtual environments. The DEAP dataset wasused within this work, along with a Support Vector Machine and Random Forest,which yielded reasonable classification accuracies for Valence and Arousalusing 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