> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nimbusbci.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Nimbus BCI Engine
> Bayesian personalization for frozen EEG encoders — wrap any trunk, adapt online with Personalizer, ship apps on BrainState. Python and Julia SDKs.
*Bayesian personalization for frozen EEG encoders — and classical BCI heads when you already have features.*
Nimbus owns the personalization layer: wrap any frozen trunk that emits `encode(X) → Z`, adapt online with a Bayesian head (`Personalizer`), and ship apps on `BrainState`. Classical CSP / bandpower / Riemann → `NimbusLDA` workflows remain fully supported.
## Start here
Encoder contract: `wrap` → `Personalizer` → `BrainState`.
Local Personalizer workflows with `nimbus-bci`.
RxInfer-backed workflows with `NimbusSDK.jl`.
## Why Nimbus
Compare classical LDA, SVM, and deep learning with Nimbus — including \~8–10× cheaper online head updates vs batch refit.
Predictions include posterior probabilities and confidence scores.
Batch and streaming workflows for low-latency BCI systems.
Bayesian heads used by Personalizer and standalone classifiers.
Validation, quality gates, diagnostics, and deployment patterns.
## Heads (used by Personalizer)
| Model | SDK | Best For |
| - | - | - |
| `NimbusLDA` | Python + Julia | Fast baseline; default `Personalizer(head="lda")`. |
| `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
**Product path (frozen trunk):**
```text theme={null}
EEG → frozen encoder → Personalizer → BrainState → confidence-aware action
```
**Classical path (features already extracted):**
```text theme={null}
EEG → preprocessing (CSP / bandpower / Riemann) → Nimbus head → confidence-aware action
```
Nimbus expects embeddings or preprocessed features — not raw EEG. Start with [Personalizer & Middleware](/personalizer/overview) for the encoder contract, or [Preprocessing Requirements](/inference-configuration/preprocessing-requirements) for classical CSP / bandpower / ERP.
## Documentation map
Encoder contract and BrainState app surface.
Choose Python or Julia and run first inference.
Breaking changes when upgrading nimbus-bci.
LDA, QDA, Softmax, Probit, STS as Personalizer heads.
Compact recipes and higher-level application patterns.
## Common questions
Python does not require an API key. Julia requires an API key to install and use the commercial core.
No. Nimbus expects embeddings from a frozen encoder (`encode(X) → Z`) or features from preprocessing pipelines such as CSP, bandpower, or ERP extraction.
Use Python for Personalizer / BrainState app integration, sklearn/MNE workflows, and local development. Use Julia for RxInfer-backed workflows and Julia-native model tooling.
## Next read
Product wedge overview
First Personalizer in minutes
Julia SDK first steps
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