Gamification of Motor Imagery Brain-Computer Interface Training Protocols: a systematic review
<|reference_start|>Gamification of Motor Imagery Brain-Computer Interface Training Protocols: a systematic review: Current Motor Imagery Brain-Computer Interfaces (MI-BCI) require a lengthy and monotonous training procedure to train both the system and the user. Considering many users struggle with effective control of MI-BCI systems, a more user-centered approach to training might help motivate users and facilitate learning, alleviating inefficiency of the BCI system. With the increase of BCI-controlled games, researchers have suggested using game principles for BCI training, as games are naturally centered on the player. This review identifies and evaluates the application of game design elements to MI-BCI training, a process known as gamification. Through a systematic literature search, we examined how MI-BCI training protocols have been gamified and how specific game elements impacted the training outcomes. We identified 86 studies that employed gamified MI-BCI protocols in the past decade. The prevalence and reported effects of individual game elements on user experience and performance were extracted and synthesized. Results reveal that MI-BCI training protocols are most often gamified by having users move an avatar in a virtual environment that provides visual feedback. Furthermore, in these virtual environments, users were provided with goals that guided their actions. Using gamification, the reviewed protocols allowed users to reach effective MI-BCI control, with studies reporting positive effects of four individual elements on user performance and experience, namely: feedback, avatars, assistance, and social interaction. Based on these elements, this review makes current and future recommendations for effective gamification, such as the use of virtual reality and adaptation of game difficulty to user skill level.<|reference_end|>
@article{atilla2024gamification,
    title={Gamification of Motor Imagery Brain-Computer Interface Training
  Protocols: a systematic review},
    author={Fred Atilla, Marie Postma, Maryam Alimardani},
    journal={arXiv preprint arXiv:2407.04610},
    year={2024},
    doi={10.1016/j.chbr.2024.100508},
    archivePrefix={arXiv},
    eprint={2407.04610},
    primaryClass={cs.HC}
}