脑机接口医疗器械的临床转化瓶颈:人机信任的生成、损耗与修复OA

Brain-computer interface(BCI),a cutting-edge technology in biomedical engineering,has revolutionized the human-machine interaction paradigm in medical devices.Nevertheless,human-machine trust remains the fundamental constraint hindering the clinical translation and widespread adoption of BCI medical devices.Current research exhibits a bias towards"hardware performance at the expense of human-machine interaction trust",lacking a systematic and integrated analysis centered on this crucial aspect.This shortcoming fails to elucidate the practical challenges encountered when high-performance laboratory solutions struggle to achieve sustained clinical implementation.In this article,we take the"generation-depletion-repair"lifecycle of human-machine trust as its logical framework,systematically examining the supportive roles of 3 underlying technologies—signal acquisition,decoding algorithms,and bidirectional closed-loop systems—in fostering human-machine trust.We also systematically analyze the disruptive mechanisms of ethical and safety risks,including neural privacy disclosure,algorithmic bias,long-term implantation-induced inflammation,and conflicts in human-machine identity recognition,on the trust system.We further summarize four types of institutional shortcomings in current technological and ethical governance.Three core strategies are proposed to address the trust dilemma:multimodal signal fusion,adaptive interpretable learning frameworks,and interdisciplinary collaborative ethical governance.Aligned with China's"two-step"development strategy and standardization efforts in the BCI industry,we outline a phased medium-to long-term development roadmap,providing theoretical underpinnings and regulatory governance references for establishing a comprehensive BCI medical device human-machine trust assurance system that spans the entire device lifecycle and balances technical reliability with ethical safety.