Abstract
A prototype system for an endless runner game controlled by a self-paced brain-computer interface (BCI) based on electroencephalography (EEG) and motor imagery (MI) is presented. In self-paced BCI systems, brain activity can be distinguished between control and non-control states, allowing the user to continuously engage with the application. The continuous nature of the system enhances the user experience and broadens the experimental setting to more real-world applications. Additionally, metrics for assessing the player’s performance during the endless runner game are introduced, including the number of collected coins, distance from the coins, and the efficiency of the avatar’s path. The system was evaluated on six subjects with varying levels of experience with MI-BCI. The results demonstrated that the proposed system can be used feasibly with as few as three calibration runs and a highly wearable low-density (8-channel) EEG cap. Furthermore, participants familiar with MI were observed to have better calibration sessions, and subsequently exhibited greater control of the endless runner game.
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Acknowledgements
This work was financially supported by Italian Ministry for Universities and Research (MUR) through the project “AGE-IT - Ageing Well in an ageing society: A novel public-private alliance to generate socioeconomic, biomedical and technological solutions for an inclusive Italian ageing society (Spoke: Ageing and clinical practice)”, PNRR PE8, CUP E63C22002050006.
Sommeling gratefully acknowledges financial support for this project by the Fulbright U.S. Student Program, sponsored by the U.S. Department of State, the U.S.-Italy Fulbright Commission, and the Fondazione Con il Sud. The paper contents are solely the responsibility of the authors and do not necessarily represent the official views of the Fulbright Program, the Government of the United States, the U.S.-Italy Fulbright Commission, or the Fondazione Con il Sud.
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Arpaia, P. et al. (2024). Endless Runner Game in Virtual Reality Controlled by a Self-paced Brain-Computer Interface Based on EEG and Motor Imagery. In: De Paolis, L.T., Arpaia, P., Sacco, M. (eds) Extended Reality. XR Salento 2024. Lecture Notes in Computer Science, vol 15028. Springer, Cham. https://doi.org/10.1007/978-3-031-71704-8_16
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