Python SDK

The nimbus-bci Python library provides sklearn-compatible Bayesian classifiers for Brain-Computer Interface applications. It delivers local probabilistic inference with uncertainty quantification, streaming support, and rich diagnostics. The optional NimbusSoftmax model uses JAX and is available with pip install nimbus-bci[softmax].

Start Here

Install nimbus-bci

Install and verify your Python SDK environment.

Run Quickstart

Build your first Bayesian BCI classifier in minutes.

Pick a Model

Compare NimbusLDA, NimbusQDA, NimbusSoftmax, and NimbusSTS.

Overview

nimbus-bci is a production-ready Python package available on PyPI that brings Bayesian inference to BCI applications with a familiar sklearn-compatible API.

pip install nimbus-bci

New in nimbus-bci 0.4.1: active-learning helpers for label-efficient calibration, including suggest_next_trial(), should_query(), and calibration_sufficient(). See Active Learning.

Key Features

sklearn Compatible

Drop-in replacement for sklearn classifiers. Works with pipelines, cross-validation, and GridSearchCV.

Streaming Inference

Real-time chunk-by-chunk processing for online BCI applications with weighted aggregation.

MNE Integration

Seamless integration with MNE-Python for EEG preprocessing and feature extraction.

Rich Diagnostics

Entropy, calibration metrics (ECE/MCE), ITR calculation, and quality assessment.

Online Learning

Update models incrementally with partial_fit() without retraining from scratch.

Active Learning

Rank unlabeled trials, request labels only when useful, and stop calibration when the posterior stabilizes.

Fully Local

No API keys required. All processing happens on your machine for maximum privacy.

Quick Example

from nimbus_bci import NimbusLDA
import numpy as np

# Create and fit classifier
clf = NimbusLDA()
clf.fit(X_train, y_train)

# Predict with uncertainty
predictions = clf.predict(X_test)
probabilities = clf.predict_proba(X_test)

# Works with sklearn pipelines
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

pipe = make_pipeline(StandardScaler(), NimbusLDA())
pipe.fit(X_train, y_train)

Available Classifiers

The Python SDK provides four Bayesian classifiers:

Classifier	Description	Best For
NimbusLDA	Bayesian LDA with shared covariance	Fast, well-separated classes (motor imagery)
NimbusQDA	Bayesian QDA with class-specific covariances	Complex distributions (P300)
NimbusSoftmax	Bayesian logistic regression (Polya-Gamma VI)	Non-Gaussian decision boundaries
NimbusSTS	Bayesian Structural Time Series (Extended Kalman Filter)	Non-stationary data, long sessions

All classifiers provide:

Full posterior distributions
Uncertainty quantification
Online learning via partial_fit()
sklearn-compatible API

Note: NimbusSTS also includes state management methods (propagate_state(), get_latent_state(), set_latent_state()) for temporal adaptation.

Python vs Julia SDK

Both SDKs implement the same Bayesian models but with different design philosophies:

Feature	Python SDK	Julia SDK
Installation	`pip install nimbus-bci`	`Pkg.add("NimbusSDK")` + API key
API Style	sklearn-compatible classes	Functional + types
Integration	sklearn pipelines, MNE-Python	RxInfer.jl ecosystem
Performance	Fast local Python implementation; optional JAX Softmax	Fastest (native Julia)
API Key	Not required	Required
Use Case	Production ML pipelines	Research, custom models
License	Proprietary (free evaluation)	Commercial tiers

When to Use Python SDK

Choose the Python SDK if you:
✅ Work primarily in Python
✅ Use sklearn pipelines and MNE-Python
✅ Want immediate installation without API keys
✅ Need sklearn-compatible API for easy integration
✅ Prefer fully local processing

When to Use Julia SDK

Choose the Julia SDK if you:
✅ Need maximum performance (native Julia)
✅ Want to build custom Bayesian models
✅ Work with RxInfer.jl ecosystem
✅ Need pre-trained model distribution
✅ Require enterprise support

Architecture

EEG Data → MNE-Python → nimbus-bci → Your Application
           (Preprocessing)  (Classification)  (Control/Analysis)

Key Design Principles:

Local Processing: All computation happens on your machine
Privacy First: Your EEG data never leaves your computer
sklearn Compatible: Works with existing ML workflows
Fast Inference: Efficient local inference, with optional JAX support for NimbusSoftmax
Uncertainty Aware: Full Bayesian posterior distributions

Use Cases

Motor Imagery BCI

Control devices with imagined movements using CSP features and NimbusLDA

P300 Speller

Communication interfaces using ERP features and NimbusQDA

Real-time Neurofeedback

Streaming inference for live brain state monitoring

Research Pipelines

sklearn-compatible API for reproducible BCI research

What’s Included

The nimbus-bci package provides: Classifiers:

NimbusLDA - Bayesian Linear Discriminant Analysis
NimbusQDA - Bayesian Quadratic Discriminant Analysis
NimbusSoftmax - Bayesian Multinomial Logistic Regression
NimbusSTS - Bayesian Structural Time Series (Extended Kalman Filter)

Data Structures:

BCIData - Container for features, metadata, labels
BCIMetadata - Metadata for BCI experiments

Inference:

predict_batch() - Batch inference with diagnostics
StreamingSession - Real-time chunk processing

Active Learning:

suggest_next_trial() - Rank unlabeled feature pools by informativeness
should_query() - Decide whether an arriving trial is worth labeling
calibration_sufficient() - Stop calibration when the posterior stabilizes

Metrics:

compute_entropy() - Prediction uncertainty
compute_calibration_metrics() - ECE/MCE
calculate_itr() - Information Transfer Rate
assess_trial_quality() - Quality checks

Utilities:

estimate_normalization_params() - Feature normalization
diagnose_preprocessing() - Preprocessing diagnostics
compute_fisher_score() - Feature discriminability

MNE Integration:

from_mne_epochs() - Convert MNE Epochs to BCIData
extract_csp_features() - CSP feature extraction
extract_bandpower_features() - Bandpower features

Next Read

Installation

Install nimbus-bci and verify your setup

Quickstart

Build your first BCI classifier in 5 minutes

API Reference

Complete API documentation with examples

Active Learning

Reduce calibration time with label-efficient trial selection

MNE Integration

Use nimbus-bci with MNE-Python

Support

Need help? We’re here for you:

Email: hello@nimbusbci.com
Documentation: Browse our comprehensive guides
GitHub: github.com/nimbusbci/nimbuspysdk
Examples: Check out working code samples in the package

Ready to get started? Head to the Installation Guide to install nimbus-bci.

Getting Started

Core Concepts

Models & Algorithms

Configuration & Setup

Examples

Development

Python SDK

Julia SDK

Documentation Index

​Python SDK

​Start Here

Install nimbus-bci

Run Quickstart

Pick a Model

​Overview

​Key Features

sklearn Compatible

Streaming Inference

MNE Integration

Rich Diagnostics

Online Learning

Active Learning

Fully Local

​Quick Example

​Available Classifiers

​Python vs Julia SDK

​When to Use Python SDK

​When to Use Julia SDK

​Architecture

​Use Cases

Motor Imagery BCI

P300 Speller

Real-time Neurofeedback

Research Pipelines

​What’s Included

​Next Read

Installation

Quickstart

API Reference

Active Learning

MNE Integration

​Support

Python SDK

Start Here

Overview

Key Features

Quick Example

Available Classifiers

Python vs Julia SDK

When to Use Python SDK

When to Use Julia SDK

Architecture

Use Cases

What’s Included

Next Read

Support