Clinical translation bottlenecks for brain-computer interface medical devices: generation, loss, and repair of human-machine trust

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.