Risk Assessment and Soft Law Responses to Brain-Computer Interface-Generated Data of Athletes

Brain-computer interface (BCI) technology holds significant promise for enhancing athletes' competitive performance. However, it poses non-negligible challenges in the domain of data governance. Employing such methods as scenario forecasting, typological analysis, and normative deduction, this study investigates the associated risks and regulatory responses. The findings reveal that BCI technology can enhance athletic performance through both direct and indirect pathways, thereby producing three types of athlete-related data from BCI systems: physiological data, generated data, and supported data. Among these, generated data serve as a pivotal enabler of BCI-driven athletic enhancement and play a crucial role in advancing both individual athlete development and competitive sports. The lifecycle of generated data comprises four sequential phases: preparation, transformation, iteration, and conclusion. A systematic risk assessment identifies multiple vulnerabilities across all stages of this lifecycle. To address these challenges, a multi-dimensional soft law framework is proposed: at the macro level, the hierarchical ordering of refined ethical and legal values should be clarified; at the meso level, a consensus-based rights-and-obligations system be established; and at the micro level, compliance mechanisms be established throughout the entire data lifecycle.