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.

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Batch Inference Configuration

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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.

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Data Contracts

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Basic Pattern

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)

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)

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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.

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Batch Diagnostics

report = diagnose_preprocessing(data)

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

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Batch vs Streaming

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