Gamification of motor imagery brain-computer interface training protocols: A systematic review

被引:2
作者
Atilla, Fred [1 ]
Postma, Marie [1 ]
Alimardani, Maryam [2 ]
机构
[1] Tilburg Univ, Dept Cognit Sci & Artificial Intelligence, Tilburg, Netherlands
[2] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands
来源
COMPUTERS IN HUMAN BEHAVIOR REPORTS | 2024年 / 16卷
关键词
Brain-computer interface; BCI inefficiency; User training; User experience; Gamification; Serious game; USER-CENTERED DESIGN; VIRTUAL-REALITY; GAME DESIGN; BCI; FEEDBACK; PERFORMANCE; ELEMENTS; VR; REHABILITATION; NEUROFEEDBACK;
D O I
10.1016/j.chbr.2024.100508
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
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.
引用
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页数:25
相关论文
共 221 条
[1]   Brain computer interfacing: Applications and challenges [J].
Abdulkader, Sarah N. ;
Atia, Ayman ;
Mostafa, Mostafa-Sami M. .
EGYPTIAN INFORMATICS JOURNAL, 2015, 16 (02) :213-230
[2]   A comprehensive review of EEG-based brain-computer interface paradigms [J].
Abiri, Reza ;
Borhani, Soheil ;
Sellers, Eric W. ;
Jiang, Yang ;
Zhao, Xiaopeng .
JOURNAL OF NEURAL ENGINEERING, 2019, 16 (01)
[3]  
Achanccaray D., 2021, Visual electrotactile stimulation feedback to improve immersive brain-computer interface based on hand motor imagery
[4]  
Achanccaray D, 2019, IEEE SYS MAN CYBERN, P2829, DOI 10.1109/SMC.2019.8913962
[5]   Immersive Virtual Reality Feedback in a Brain Computer Interface for Upper Limb Rehabilitation [J].
Achanccaray, David ;
Pacheco, Kevin ;
Carranza, Erick ;
Hayashibe, Mitsuhiro .
2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2018, :1006-1010
[6]   Signal processing techniques for motor imagery brain computer interface: A review [J].
Aggarwal, Swati ;
Chugh, Nupur .
ARRAY, 2019, 1-2
[7]  
Ahmad M., 2019, Categorizing game design elements into educational game design fundamentals game Design and intelligent interaction (IntechOpen)
[8]  
Ahn M., 2014, A review of brain-computer interface games and an opinion survey from researchers, developers and users sensors, V14, P14601
[9]   A systematic rank of smart training environment applications with motor imagery brain-computer interface [J].
Al-Qaysi, Z. T. ;
Ahmed, M. A. ;
Hammash, Nayif Mohammed ;
Hussein, Ahmed Faeq ;
Albahri, A. S. ;
Suzani, M. S. ;
Al-Bander, Baidaa .
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (12) :17905-17927
[10]   A multi-modal modified feedback self-paced BCI to control the gait of an avatar [J].
Alchalabi, Bilal ;
Faubert, Jocelyn ;
Labbe, David R. .
JOURNAL OF NEURAL ENGINEERING, 2021, 18 (05)