From technological closed-loop to human-machine trust: a systematic review of ethical and communication challenges of...

As the global population ages, brain-computer interfaces (BCIs) have emerged as a promising technological pathway for restoring communication, motor function, and cognitive support in elderly care settings. However, the ethical and communication challenges accompanying BCI deployment in these contexts remain insufficiently understood. This systematic review identified 177 studies meeting the inclusion criteria from 12,423 records. Through comprehensive analysis, six core ethical themes were identified: privacy and data security, informed consent and autonomy, personhood and identity, technical risks and safety, equity and accessibility, and risk-benefit trade-offs. Beyond cataloging these ethical challenges, this review proposes a three-level analytical framework encompassing human-computer interaction, interpersonal communication, and public opinion dissemination to examine how ethical risks manifest as communication breakdowns across the closed-loop BCI process of neural signal acquisition, intention decoding, and external feedback. Furthermore, a three-dimensional mapping integrating ethical issues, communication challenges, and application scenarios (rehabilitation assistance, communication assistance, monitoring and prevention, and emotional support) is constructed to reveal scenario-specific ethical configurations. The findings indicate that BCI's ethical risks are not peripheral but structurally embedded in the technology's interaction logic-decoding uncertainty can translate into care misjudgment, neural data inferability extends privacy risks beyond information leakage to psychological exposure and power asymmetry, and technology-mediated communication creates structural tensions in trust and responsibility. The review concludes that responsible BCI deployment in elderly care requires not merely algorithmic improvement but the systematic engineering of ethical principles into care processes, including uncertainty disclosure at the interface level, data minimization and layered informed consent at the institutional level, and interdisciplinary collaboration with long-term support systems.