Design and Application of Digital Twin-Based Brain Control System - Yinan Wang; Hongyang Jin; Zongwei Yao; Zhiyong Chang; Deping Wang (Journal of Advanced Transportation)
Despite the development and application of brain-computer interface (BCI) across various fields, this technology continues to face numerous challenges. The limitations in hardware and algorithm performance result in low recognition rates of BCI commands, hindering the system’s ability to perform efficiently and reliably, thus failing to meet the safety requirements. Digital twin (DT) technology, with its ultra-high-fidelity simulation and prediction capabilities, virtual–real interaction mapping, and autonomous feedback regulation, offers a novel approach to addressing these issues. Therefore, this paper proposes a DT-based BCI (DT-BCI) system framework, using a brain-controlled vehicle as a case study to detail the roles and functions of each element within the framework. Meanwhile, the results of obstacle avoidance experiments show that the DT-BCI system improves the task completion rate by 37.5% compared with the traditional brain–computer interface (T-BCI), which proves that the DT technology has an important prospect for brain control applications, and lays the foundation for its wider application in complex operational scenarios.