关键词：脑机接口；体质指数；解码算法；闭环系统
摘 要：Brain-machine interfaces (BMIs) aim to assist patients suffering from neurological injuries and disease by enabling them to use their own neural activity to control external devices such as computer cursors or robotic arms, or even drive movements of their own body via muscle stimulation. At the heart of a BMI system is the decoding algorithm, or “decoder”, that translates recorded neural activity into control signals for a prosthetic device. Decoders are often initialized offline by first recording neural activity while a subject performs real movements, or observes or imagines movements, and then fitting a decoder to predict these movements from the neural activity. However, BMIs are fundamentally closed-loop systems, since BMI users receive performance feedback (e.g. by visual observation of the prosthetic’s movements), and the prediction power of decoders trained offline does not directly correlate with closed-loop performance. In other words, a high level of BMI performance can not necessarily be achieved solely by optimizing decoder parameters in an open-loop setting.