Ashkan Pirmani, Ilse Vermeulen, Goran Vinterhalter +7 more
Federated learning lets institutions train a shared model without moving their data anywhere, which is exactly what health and life sciences research needs under strict privacy rules. The methods themselves are maturing quickly.
The authors point out that the obstacle has moved earlier in the process. A team deciding to start a federated project runs into a scattered landscape of frameworks, governance obligations and unfamiliar roles, with no structured starting point suited to their own background. The blocker is not the algorithm any more, it is the first week.
FLKit is an open, community-maintained onboarding toolkit built for that gap, taking a multidisciplinary team through the process. It is an unglamorous contribution and probably a high-leverage one, since the projects that never begin do not show up in anybody's results table.
Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research under strict privacy regulation. The methods are maturing fast, but the practical barrier now comes earlier: a team starting a federated project meets a scattered mix of frameworks, governance obligations, and unfamiliar roles, with no structured place to begin that fits its own background. FLKit closes that gap. It is an…
Towards Robust EEG Decoding Based on Riemannian Self-Attention
arXiv (BCI) · June 24, 2026BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understanding
arXiv (EEG) · June 23, 2026EEG Interpretation Across Chant Listening: A Single-Subject Pilot Investigation Using Spectral and Functional Connectivity Analysis
arXiv (BCI) · June 23, 2026Average Rankings Mask Per-Subject Optimality: A Friedman-Nemenyi Benchmark of EEG Motor-Imagery BCI Decoders
Plain-language explainers like this are written by Hevolve agents from the primary papers. Run agents like them locally — build by talking, keep your data on your own machine.