Real-Time BCI Setup

This page owns hardware and acquisition setup: how EEG gets from an amplifier into embedding or feature chunks that Nimbus can consume. For SDK streaming APIs, see Streaming Inference Configuration, Python Streaming Inference, and Julia Streaming Inference.

Online encode path: amplifier → preprocess → frozen trunk encode → embedding chunks → Personalizer / streaming session → BrainState.
Classical path: amplifier → preprocess → CSP/bandpower chunks → head / streaming session.

Real-Time Pipeline

EEG amplifier -> acquisition transport -> preprocess / encode -> chunks -> Nimbus (Personalizer / streaming) -> application command

Nimbus expects embedding or feature chunks, not raw EEG. Your acquisition loop should filter, clean, window, and either encode or extract features before calling the SDK.

Acquisition Options

Option	Best for	Notes
Lab Streaming Layer (LSL)	Research labs and multi-device synchronization	Common for EEG streams and markers
BrainFlow	Consumer and research EEG hardware	Unified API for many boards
Vendor SDK	Production devices	Lowest integration overhead when the vendor API is stable
File replay	Testing and demos	Useful for deterministic latency tests

Setup Checklist

Confirm amplifier sampling rate and channel order.
Synchronize event markers with EEG samples.
Apply artifact handling and feature extraction in the same way as calibration.
Emit chunks with stable shape: (n_features, chunk_size).
Use the same normalization parameters from training.
Measure latency around preprocessing and SDK inference separately.
Add rejection thresholds for low-confidence or high-entropy decisions.

LSL Pattern

Use LSL when you need synchronized EEG and marker streams.

from pylsl import StreamInlet, resolve_stream

eeg_stream = resolve_stream("type", "EEG")[0]
marker_stream = resolve_stream("type", "Markers")[0]

eeg_inlet = StreamInlet(eeg_stream)
marker_inlet = StreamInlet(marker_stream)

while True:
    samples, timestamps = eeg_inlet.pull_chunk(timeout=0.0)
    markers, marker_times = marker_inlet.pull_chunk(timeout=0.0)

    if samples:
        feature_chunk = preprocess_to_features(samples, markers)
        result = session.process_chunk(feature_chunk)
        handle_prediction(result.prediction, result.confidence)

BrainFlow Pattern

Use BrainFlow when you want one API across supported EEG boards.

from brainflow.board_shim import BoardShim, BrainFlowInputParams, BoardIds

params = BrainFlowInputParams()
board = BoardShim(BoardIds.CYTON_BOARD.value, params)

board.prepare_session()
board.start_stream()

try:
    while True:
        data = board.get_current_board_data(250)
        feature_chunk = preprocess_to_features(data)
        result = session.process_chunk(feature_chunk)
        handle_prediction(result.prediction, result.confidence)
finally:
    board.stop_stream()
    board.release_session()

Latency Budget

Stage	Target
Acquisition buffer	1-10ms
Preprocessing and feature extraction	5-50ms, depending on feature type
Nimbus inference	10-25ms typical
Application command	1-10ms

Profile each stage separately. If latency is high, the bottleneck is often acquisition buffering or feature extraction rather than model inference.

Production Guardrails

Warm up model and preprocessing code before the session starts.
Use fixed-size buffers to avoid unbounded memory growth.
Reset streaming sessions between trials.
Log per-stage latency, confidence, entropy, and rejection decisions.
Keep calibration preprocessing and online preprocessing identical.

Next Read

Streaming Inference Configuration

Choose chunk sizes and aggregation methods.

Python Streaming Inference

Use StreamingSession in Python.

Julia Streaming Inference

Use init_streaming, process_chunk, and finalize_trial in Julia.

Preprocessing Requirements

Prepare feature-space inputs correctly.

Getting started

Personalizer

Python SDK

Inference & pipelines

Heads & models

Concepts

Julia SDK

Cloud API

Examples

Real-Time BCI Setup

Real-Time BCI Setup

Real-Time Pipeline

Acquisition Options

Setup Checklist

LSL Pattern

BrainFlow Pattern

Latency Budget

Production Guardrails

Next Read

Streaming Inference Configuration

Python Streaming Inference

Julia Streaming Inference

Preprocessing Requirements

​Real-Time BCI Setup

​Real-Time Pipeline

​Acquisition Options

​Setup Checklist

​LSL Pattern

​BrainFlow Pattern

​Latency Budget

​Production Guardrails

​Next Read

Streaming Inference Configuration

Python Streaming Inference

Julia Streaming Inference

Preprocessing Requirements

Real-Time BCI Setup

Real-Time Pipeline

Acquisition Options

Setup Checklist

LSL Pattern

BrainFlow Pattern

Latency Budget

Production Guardrails

Next Read