Multiplayer Collaborative Game Using Cloud Computing Based Brain-Computer Interface
Fan Zhang, Jiangmao Zheng, Tao Wang · 2024
Brain-computer interface (BCI) technology has garnered significant attention in the realm of game control, showcasing its immense potential. By leveraging collected electroencephalogram (EEG) signals and body movements captured by machine vision for game interaction, it presents innovative possibilities for enhancing user experience and creating new interactive platforms. However, practical applications of these new technologies still face numerous challenges, such as achieving real-time performance, managing large-scale data, and handling heterogeneous data from BCI and game sources. While traditional signal processing methods are quite efficient in localized contexts, they struggle to meet the demands in multi-user remote collaborative scenarios for BCI controlled games. This paper aims to achieve the task of remote multi-user consciousness collaboration control using “cloud-based BCI”. In this paper, we propose a cloud-based BCI method that allows multiple users to collaboratively control the game environment's logic through real-time EEG signals. This approach makes large-scale remote multi-user consciousness collaboration a cost-effective possibility and enhances the overall gaming experience. This method still has significant potential for improvement and provides reliable game control and optimization of user interface to achieve goals of low latency, high throughput, and ease of use.