> ## 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.

# Industry Use Cases

> Real-world BCI use-case patterns across healthcare, assistive technology, research, and productivity using Nimbus Bayesian models.

# Industry Use Cases

This page maps common BCI product scenarios to Nimbus model and workflow choices. It is intentionally pattern-focused; use [Basic Examples](/examples/basic-examples) and [Advanced Applications](/examples/advanced-applications) for implementation recipes.

## Healthcare And Rehabilitation

**Scenario:** motor imagery rehabilitation after stroke or injury.

Recommended pattern:

* Use CSP features from motor imagery sessions.
* Start with `NimbusLDA` for fast baseline feedback.
* Use confidence thresholds to decide whether feedback should be shown.
* Track session-level accuracy, confidence, fatigue indicators, and rejection rate.
* Keep a clinician-visible audit trail for model version, calibration data, and session metrics.

Key docs:

* [Real-Time BCI Setup](/inference-configuration/real-time-setup)
* [Error Handling](/inference-configuration/error-handling)
* [Bayesian LDA](/models/rxlda)

## Assistive Communication

**Scenario:** P300 or event-related communication interfaces.

Recommended pattern:

* Use ERP amplitude features in a consistent post-stimulus window.
* Prefer `NimbusQDA` when target and non-target distributions overlap.
* Use posterior confidence to decide between accept, confirm, or repeat.
* Log rejected selections separately from incorrect selections.

Key docs:

* [Basic Examples: P300 Detection](/examples/basic-examples#p300-detection)
* [Bayesian QDA](/models/rxgmm)
* [Feature Normalization](/inference-configuration/feature-normalization)

## Wheelchair Or Device Control

**Scenario:** safety-critical control from motor imagery or hybrid BCI commands.

Recommended pattern:

* Separate classifier output from command execution.
* Require higher confidence for movement than for UI navigation.
* Add a confirmation state for ambiguous commands.
* Stop the control loop after repeated low-quality chunks.
* Monitor latency budget end to end: acquisition, preprocessing, inference, and action.

Key docs:

* [Streaming Inference Configuration](/inference-configuration/streaming-inference)
* [Julia Streaming Inference](/julia-sdk/streaming-inference)
* [Python Streaming Inference](/python-sdk/streaming-inference)

## Neurofeedback And Training

**Scenario:** live feedback for attention, motor imagery, or cognitive training.

Recommended pattern:

* Use streaming inference with smoothing in the application layer.
* Keep the classifier probabilistic and expose confidence.
* Adapt difficulty based on recent performance, not a single prediction.
* Use `NimbusSTS` for Python workflows with long-session drift.

Key docs:

* [Bayesian STS](/models/rxsts)
* [Advanced Applications](/examples/advanced-applications)
* [Probabilistic AI & Uncertainty](/core-concepts/probabilistic-ai)

## Research Studies

**Scenario:** offline experiments, multi-subject evaluation, or model comparison.

Recommended pattern:

* Use batch processing for reproducible offline analysis.
* Evaluate with subject-wise or session-wise splits when studying generalization.
* Save preprocessing and normalization parameters with every experiment.
* Compare `NimbusLDA` against `NimbusQDA` before moving to more specialized models.

Key docs:

* [Batch Inference Configuration](/inference-configuration/batch-processing)
* [Model Specification](/model-specification)
* [External Preprocessing Integration](/development/preprocessing-integration)

## Product Analytics

For deployed BCI systems, monitor:

| Metric           | Why It Matters                                            |
| ---------------- | --------------------------------------------------------- |
| Accuracy         | Measures task success when labels are available.          |
| Mean confidence  | Tracks model certainty and signal quality.                |
| Rejection rate   | Shows whether thresholds or preprocessing are too strict. |
| Latency          | Protects real-time user experience.                       |
| Calibration age  | Indicates when retraining may be needed.                  |
| Drift indicators | Flags electrode, fatigue, or session changes.             |

## Choosing A Starting Point

| Use Case                | First Model          | Workflow                                            |
| ----------------------- | -------------------- | --------------------------------------------------- |
| Motor imagery rehab     | `NimbusLDA`          | Streaming with confidence gates.                    |
| P300 communication      | `NimbusQDA`          | Batch calibration, then confidence-gated selection. |
| Long neurofeedback      | `NimbusSTS`          | Stateful Python streaming.                          |
| Julia multinomial tasks | `NimbusProbit`       | Julia training and streaming workflows.             |
| Cross-subject research  | `NimbusLDA` baseline | Batch evaluation with strict normalization.         |

## Next Read

<CardGroup cols={2}>
  <Card title="Basic Examples" icon="play" href="/examples/basic-examples">
    Compact recipes for common BCI tasks.
  </Card>

  <Card title="Advanced Applications" icon="rocket" href="/examples/advanced-applications">
    Cross-subject, hybrid, adaptive, and robust deployment patterns.
  </Card>

  <Card title="Model Specification" icon="brain" href="/model-specification">
    Choose a model family for your use case.
  </Card>

  <Card title="Development Workflow" icon="code" href="/development">
    Production project structure and guardrails.
  </Card>
</CardGroup>