The Mechanism of Brain-Computer Interfaces: Converting Motor Cortex Neural Signals into Robotic Arm Control Commands
TL;DR
Analysis shows that the collaborative optimization of signal quality, decoding algorithm and shared control strategy is the key to improving the naturalness and reliability of robotic arm operation.
Abstract
Brain-computer interfaces (BCIs) can provide patients with severe motor disorders with a new way to bypass damaged nerve pathways and control external devices by decoding the nerve activity of the motor cortex. Current research has made progress in continuous movement, multi-degree-of-freedom control and sensory feedback, but long-term stability, control accuracy and clinical safety are still limited. This paper analyses the signal acquisition characteristics of invasive, non-invasive and intravascular BCIs, sorts out the signal preprocessing, feature extraction and neural decoding processes, and summarizes the generation methods of continuous, discrete and multi-degree-of-freedom robotic arm instructions. Analysis shows that the collaborative optimization of signal quality, decoding algorithm and shared control strategy is the key to improving the naturalness and reliability of robotic arm operation. This article provides a systematic reference for the design and research of the BCI robotic arm system. Future research should focus on developing stable neural interfaces, adaptive individual decoding, computer vision assistance and two-way sensory feedback to promote their long-term clinical and daily applications.