Progress and Challenges in Brain-Computer Interface-Based Hand Rehabilitation Robots

Converging trends in brain-computer interfaces (BCIs) and rehabilitation robotics have led to rapid advancements in BCI technology, positioning it as a promising innovation in the medical field. As a technology that establishes a direct connection between the human brain and computer systems, brain-computer interfaces are designed to interpret brainwave signals. These interfaces translate neural activity into commands that facilitate the control of external devices. As different development trajectories are followed, advancements in intelligent algorithms and machine learning technologies suggest that future BCI-based hand rehabilitation robots may achieve personalized training. These systems have the capacity to make precise adjustments to training protocols in real time based on brainwave signals. This adaptive training model has the potential to enhance treatment efficacy, increase patient engagement, and promote patient initiative in the rehabilitation process, thereby improving overall recovery outcomes. The integration of BCI with other advanced technologies is a future trend that merits further investigation. This paper reviews the current research status, advantages, and limitations of BCI-based hand rehabilitation robots. The analysis method employed in this study involves the identification and conclusion of the existing development characteristics and future trends of BCI-based hand rehabilitation robots for various applications under different development directions.