Resources & Documentation
Explore guides, API documentation, research papers, and tutorials to accelerate your BCI projects.
Python SDK Documentation
Complete guide to nimbus-bci: installation, API reference, examples, and best practices for sklearn-compatible BCI inference.
Julia SDK Documentation
NimbusSDK.jl reference: reactive Bayesian inference with sub-20ms latency for high-performance BCI applications.
Nimbus Studio Guide
Learn how to use the visual pipeline builder, export code, and work with various EEG hardware devices.
Nimbus MCP Server
Open-source MCP server: run Studio pipelines, experiment campaigns, and live EEG sessions from Claude Code, Cursor, or Claude Desktop.
Universal BCI Personalization (Paper 1)
arXiv preprint (arXiv:2607.22397): one Personalizer API across frozen EEG trunks and foundation models — exploratory multi-trunk evidence and cost vs fine-tune.
A Resource Ladder for Adapting Frozen BCI Encoders (Paper 2)
arXiv companion preprint: when should deployed decoders adapt? Resource-priced ladder, regime-aware gating, and Conductor session drift governance.
Getting Started with Python SDK
Quick start tutorial: Install nimbus-bci, train your first BCI model, and run real-time inference in minutes.
Active Inference & Probabilistic AI
Understand the theory behind Nimbus: Active Inference, Bayesian inference, and uncertainty quantification in BCI.
Book a Demo
Schedule a personalized demo with our team to see Nimbus in action and discuss your specific BCI use case.
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