Julia Streaming Inference

Streaming inference processes preprocessed feature chunks as they arrive from your BCI pipeline. It runs locally in NimbusSDK.jl; no network call is made during chunk inference.

For Python streaming, see Python SDK Streaming Inference. For cross-SDK configuration guidance, see Streaming Inference Configuration.

When To Use It

Use Julia streaming when your application needs low-latency feedback before a full trial is complete:

Real-time motor imagery control
Neurofeedback and training applications
Assistive interfaces with confidence-based rejection
Long-running monitoring sessions

The Julia SDK streaming API supports NimbusLDA, NimbusQDA, and NimbusProbit.

Streaming Flow

EEG hardware -> preprocessing -> feature chunks -> NimbusSDK.jl -> predictions

Nimbus expects feature chunks, not raw EEG. Filtering, artifact handling, and feature extraction should happen before calling process_chunk().

Basic Setup

using NimbusSDK

# One-time setup. The core can be cached for later use.
NimbusSDK.install_core("your-api-key")

model = load_model(NimbusLDA, "motor_imagery_4class_v1")

metadata = BCIMetadata(
    sampling_rate = 250.0,
    paradigm = :motor_imagery,
    feature_type = :csp,
    n_features = 16,
    n_classes = 4,
    chunk_size = 250
)

session = init_streaming(model, metadata)

Process Chunks

Each chunk should be shaped (n_features, chunk_size).

for chunk in eeg_feature_stream
    result = process_chunk(session, chunk; iterations=10)

    println("Prediction: $(result.prediction)")
    println("Confidence: $(round(result.confidence, digits=3))")
    println("Posterior: $(result.posterior)")
end

Finalize A Trial

After enough chunks have been processed for a trial, aggregate them into a final prediction:

final = finalize_trial(session; method=:weighted_vote)

if should_reject_trial(final.confidence, 0.7)
    @warn "Trial rejected" confidence=final.confidence
else
    @info "Trial accepted" prediction=final.prediction confidence=final.confidence
end

Supported aggregation methods:

:weighted_vote: weight chunk predictions by confidence.
:max_confidence: use the prediction from the most confident chunk.
:posterior_mean: average chunk-level posterior distributions.
:unanimous: require all chunks to agree, with fallback behavior when they do not.

Chunk Size Guidance

Paradigm	Typical Chunk Size	Notes
Motor imagery	250-500 samples	Balances latency and accuracy at 250 Hz.
P300	100-200 samples	Short windows for event-related responses.
SSVEP	500-1000 samples	Longer windows help frequency estimates.

Smaller chunks reduce latency but can lower per-chunk confidence. Larger chunks improve evidence quality but delay feedback.

Production Checklist

Validate each chunk shape before calling process_chunk().
Warm up the model with a dummy chunk before the user session.
Use a confidence threshold for high-stakes actions.
Keep preprocessing and feature extraction deterministic between calibration and deployment.
Reset or recreate sessions between independent trials when you do not want chunk history to carry over.

Next Read

Real-time Setup

Acquisition and hardware setup guidance.

Julia SDK API Reference

Complete Julia SDK function reference.

Batch Processing

Offline trial-level inference patterns.

Preprocessing Requirements

Feature preparation requirements before inference.

Getting started

Personalizer

Python SDK

Inference & pipelines

Heads & models

Concepts

Julia SDK

Cloud API

Examples

Julia Streaming Inference

Julia Streaming Inference

When To Use It

Streaming Flow

Basic Setup

Process Chunks

Finalize A Trial

Chunk Size Guidance

Production Checklist

Next Read

Real-time Setup

Julia SDK API Reference

Batch Processing

Preprocessing Requirements

​Julia Streaming Inference

​When To Use It

​Streaming Flow

​Basic Setup

​Process Chunks

​Finalize A Trial

​Chunk Size Guidance

​Production Checklist

​Next Read

Real-time Setup

Julia SDK API Reference

Batch Processing

Preprocessing Requirements

Julia Streaming Inference

When To Use It

Streaming Flow

Basic Setup

Process Chunks

Finalize A Trial

Chunk Size Guidance

Production Checklist

Next Read