Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot Study

Abstract

Physical and cognitive rehabilitation is deemed crucial to attenuate symptoms and toimprove the quality of life in people with neurodegenerative disorders, such as Parkinson’s Disease.Among rehabilitation strategies, a novel and popular approach relies on exergaming: the patientperforms a motor or cognitive task within an interactive videogame in a virtual environment. Thesestrategies may widely benefit from being tailored to the patient’s needs and engagement patterns. Inthis pilot study, we investigated the ability of a low-cost BCI based on single-channel EEG to measurethe user’s engagement during an exergame. As a first step, healthy subjects were recruited to assessthe system’s capability to distinguish between (1) rest and gaming conditions and (2) gaming atdifferent complexity levels, through Machine Learning supervised models. Both EEG and eye-blinkfeatures were employed. The results indicate the ability of the exergame to stimulate engagementand the capability of the supervised classification models to distinguish resting stage from game-play(accuracy > 95%). Finally, different clusters of subject responses throughout the game were identified,which could help define models of engagement trends. This result is a starting point in developingan effectively subject-tailored exergaming system.