Controlling a Mini Game using a Brain-Computer Interface

Abstract

The progress in Brain-Machine Interface technology has paved the way for innovative applications in various fields, including gaming. This investigation explores the growth and implementation of a novel BCI system for controlling a mini- game, showcasing the potential of direct brain-to-machine interaction in the gaming domain. The proposed system employs non-invasive electroencephalography (EEG) sensors to capture brain signals associated with specific mental commands. These signals are then processed using advanced signal processing techniques to extract meaningful features. Machine learning algorithms, such as classification models, are trained on these features to recognize and interpret user intent in real-time. To demonstrate the practicality of the BCI-controlled mini- game, a custom designed gaming environment is introduced. Users navigate and interact within the game solely through their mental commands, eliminating the need for traditional input devices. The mini-game serves as a platform to assess the accuracy, responsiveness, and user experience of the BCI system in a dynamic and engaging context. The study evaluates the BCI system’s performance through user trials, analyzing factors such as accuracy, speed, and user satisfaction. Additionally, potential challenges and limitations of the BCI-controlled mini-game are discussed, and avenues for future research and improvement are explored. This research contributes to the growing body of knowledge in BCI technology by showcasing its applicability in the gaming realm. The findings not only provide insights into the feasibility of using BCIs for interactive entertainment but also contribute to the ongoing efforts to enhance the accessibility and inclusivity of gaming experiences through innovative technological solutions.