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

TL;DR

This paper evaluates EEG features for emotion recognition in virtual environments, aiming to improve user interaction by modeling affective states using machine learning on the DEAP dataset.

Contribution

It introduces an analysis of EEG features for affective state classification in virtual environments, providing a foundation for future emotion-aware interaction systems.

Findings

Support Vector Machine and Random Forest achieved reasonable accuracy in classifying Valence and Arousal.

Features based on statistical measures, band power, and High Order Crossing were effective.

The study demonstrates the potential of EEG-based emotion recognition to enhance virtual environment interactions.

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.