BCIwiki (bciwiki.com) — Researchers at Tsinghua University have proposed TrustBCI, a platform for collaborative exchange of brain-computer interface data between institutions, reported in Proceedings of the VLDB Endowment on September 14, 2026. The team notes that high-quality BCI datasets are essential for developing accurate and generalizable models, yet in practice they remain isolated across institutions, heterogeneous in format and difficult to share under privacy constraints. Existing BCI data platforms address only part of the problem, the authors write, and offer no unified support for data contribution, privacy-preserving computation and downstream use.
According to the paper, TrustBCI combines three components: assetization tools that prepare raw BCI datasets, incentive mechanisms intended to sustain data contribution, and privacy-aware services that let users work with protected data without exposing raw records. The demonstration focuses on two representative workflows, controllable data synthesis and privacy-preserving data query, and shows dataset-level and query-level access to protected BCI data through a functional web prototype. The authors are Jianyu Yang, Jiannan Wang and Guoliang Li. More broadly, the team writes, TrustBCI illustrates how protected domain data can be turned into usable data services through a unified data platform.
The platform is presented as a demonstration prototype. The paper does not disclose how many institutions participate, the volume of data involved, any deployment, or performance overhead figures for privacy-preserving computation on BCI data.