> ## 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.

# Batch Inference Configuration

> Configure Nimbus for offline BCI inference, calibration, validation, and research analysis across multiple trials.

# Batch Inference Configuration

Batch inference processes complete trials offline. Use it for calibration, validation, model comparison, research studies, and quality review. Use [Streaming Inference Configuration](/inference-configuration/streaming-inference) when predictions must update during a live trial.

## When To Use Batch

* Train or calibrate a model on labeled trials.
* Evaluate held-out sessions or subjects.
* Compare model families and hyperparameters.
* Run preprocessing diagnostics before deployment.
* Analyze confidence, entropy, accuracy, and ITR after a session.

## Data Contracts

| SDK                        | Typical Batch Shape                 | Notes                                    |
| -------------------------- | ----------------------------------- | ---------------------------------------- |
| Python classifiers         | `(n_trials, n_features)`            | sklearn-compatible estimator API.        |
| Python `BCIData` utilities | `(n_features, n_samples, n_trials)` | Used by lower-level inference helpers.   |
| Julia `BCIData`            | `(n_features, n_samples, n_trials)` | Labels are typically 1-indexed integers. |

All batch workflows should use preprocessed features, not raw EEG.

## Basic Pattern

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    from nimbus_bci import NimbusLDA
    from sklearn.model_selection import cross_val_score

    X = load_features()  # (n_trials, n_features)
    y = load_labels()

    clf = NimbusLDA()
    scores = cross_val_score(clf, X, y, cv=5)

    clf.fit(X, y)
    probabilities = clf.predict_proba(X)
    predictions = clf.predict(X)
    ```
  </Tab>

  <Tab title="Julia">
    ```julia theme={null}
    using NimbusSDK
    using Statistics

    features = load_features()  # (n_features, n_samples, n_trials)
    labels = load_labels()

    metadata = BCIMetadata(
        sampling_rate = 250.0,
        paradigm = :motor_imagery,
        feature_type = :csp,
        n_features = size(features, 1),
        n_classes = length(unique(labels)),
        chunk_size = nothing
    )

    data = BCIData(features, metadata, labels)
    model = train_model(NimbusLDA, data; iterations=50)
    results = predict_batch(model, data)

    accuracy = mean(results.predictions .== labels)
    ```
  </Tab>
</Tabs>

## Evaluation Checklist

* Split by session or subject when testing generalization.
* Estimate normalization parameters on training folds only.
* Report accuracy with confidence and rejection rate.
* Inspect posterior entropy for uncertain trials.
* Compare against a simple `NimbusLDA` baseline before tuning.

## Batch Diagnostics

Run diagnostics when batch accuracy is unexpectedly low:

```julia theme={null}
report = diagnose_preprocessing(data)

if !isempty(report.errors)
    error("Invalid batch data: $(report.errors)")
end
```

Common issues include wrong feature shape, raw EEG passed as features, inconsistent label encoding, missing normalization, and mismatched model metadata.

## Batch vs Streaming

| Need                           | Use       |
| ------------------------------ | --------- |
| Offline calibration            | Batch     |
| Cross-validation               | Batch     |
| Research analysis              | Batch     |
| Live feedback                  | Streaming |
| Chunk-by-chunk command updates | Streaming |

Most production BCI systems use both: batch for calibration and validation, streaming for live operation.

## Next Read

<CardGroup cols={2}>
  <Card title="Feature Normalization" icon="arrows-up-down" href="/inference-configuration/feature-normalization">
    Keep train/test/deployment scales consistent.
  </Card>

  <Card title="Streaming Inference" icon="activity" href="/inference-configuration/streaming-inference">
    Configure live chunk processing.
  </Card>

  <Card title="Basic Examples" icon="play" href="/examples/basic-examples">
    Compact batch and streaming recipes.
  </Card>

  <Card title="Model Specification" icon="brain" href="/model-specification">
    Choose a model before evaluation.
  </Card>
</CardGroup>