Evaluating Player Task Engagement and Arousal Using Electroencephalography

Advances in affective computing technologies have made it possible for researchers to investigate brain function while users interact in virtual environments. Progress in sensors and algorithms for off-the-shelf EEG systems has made it possible for gaming researchers to perform real-time estimation of human cognitive and affective states using EEG. In this study our aim was to coordinate “Task Engagement” data with “Arousal-Valence” data. The resulting coordinate was designed for application to expressive transformations to video game play in real time by tuning different performance parameters in an Engagement-Arousal rule system. Results revealed that the engagement index (Beta / (Alpha + Theta)) was capable of differentiating high intensity game events (Player Death) from general game play. Given that higher levels of engagement during death events may reflect increase in autonomic response, we also measured arousal by using (BetaF3 + BetaF4) / (AlphaF3 + AlphaF4) and valence using (AlphaF4 / BetaF4) - (AlphaF3 / BetaF3). Results revealed that arousal increases and valence decreases during high intensity game events (Player Death) when compared to lower intensity game events (General Game Play). Given our desire to establish “Task Engagement” data with “Arousal-Valence” coordinates for a flow model, we divided the data into quartiles, which allowed us to establish upper and lower thresholds to indicate when the player has left a state of flow. Our aim was to use an off-the-shelf EEG system to establish “Task Engagement” and “Arousal-Valence” coordinates during video game play that can be used for a flow model. It is believed that this model will allow for future use of the Emotiv for assessing the cognitive and emotional processing of the player.