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Python SDK
Local sklearn-compatible workflows with
nimbus-bci.Julia SDK
RxInfer-backed workflows with
NimbusSDK.jl.Model Selection
Choose between LDA, QDA, Softmax, Probit, and STS.
Why Nimbus
Probabilistic Outputs
Predictions include posterior probabilities and confidence scores.
Real-Time Inference
Batch and streaming workflows for low-latency BCI systems.
BCI-Specific Models
Bayesian model families tuned for neural feature data.
Production Guardrails
Validation, quality gates, diagnostics, and deployment patterns.
Models
| Model | SDK | Best For |
|---|---|---|
NimbusLDA | Python + Julia | Fast baseline for well-separated CSP or bandpower features. |
NimbusQDA | Python + Julia | Overlapping classes and class-specific covariance. |
NimbusSoftmax | Python | Multiclass nonlinear boundaries with optional JAX install. |
NimbusProbit | Julia | Julia-native Bayesian multinomial probit workflows. |
NimbusSTS | Python | Non-stationary sessions and latent-state adaptation. |
Core Workflow
Documentation Map
Installation And Quickstarts
Choose Python or Julia and run first inference.
Configuration
Preprocessing, normalization, batch, streaming, and error handling.
Examples
Compact recipes and higher-level application patterns.
API References
SDK-specific functions, classes, and data structures.
Common Questions
Do I need an API key?
Do I need an API key?
Python does not require an API key. Julia requires an API key to install and use the commercial core.
Can Nimbus process raw EEG?
Can Nimbus process raw EEG?
No. Nimbus expects features produced by preprocessing pipelines such as CSP, bandpower, or ERP extraction.
Which SDK should I start with?
Which SDK should I start with?
Use Python for sklearn/MNE workflows and local development. Use Julia for RxInfer-backed workflows and Julia-native model tooling.