# Nimbus ## Docs - [Cloud API Authentication](https://docs.nimbusbci.com/cloud-api/authentication.md): Authenticate Nimbus Cloud API requests with API keys, license validation, and secure usage patterns for Julia SDK integrations. - [Cloud API Endpoints](https://docs.nimbusbci.com/cloud-api/inference-endpoints.md): Reference for Nimbus Cloud API endpoints covering model registry access, inference operations, analytics, and usage workflows. - [Cloud API Reference](https://docs.nimbusbci.com/cloud-api/introduction.md): Cloud API reference for Nimbus authentication, licensing, model registry, and backend endpoints used by the Julia SDK. - [Message Passing Architecture](https://docs.nimbusbci.com/core-concepts/message-passing.md): Understanding the reactive message passing architecture in Nimbus. Learn about factor graphs, belief propagation, and RxInfer.jl reactive inference. - [Probabilistic AI & Uncertainty](https://docs.nimbusbci.com/core-concepts/probabilistic-ai.md): Learn how Nimbus uses Bayesian inference, posterior probabilities, confidence scores, and quality checks to make BCI systems more reliable. - [Nimbus Development Workflow](https://docs.nimbusbci.com/development.md): Build production BCI applications with Nimbus using practical project structure, testing, debugging, and deployment guidance. - [External Preprocessing Integration](https://docs.nimbusbci.com/development/preprocessing-integration.md): Export preprocessed EEG features from MNE-Python, EEGLAB, OpenViBE, or MATLAB and load them into Nimbus SDK workflows. - [Advanced Applications](https://docs.nimbusbci.com/examples/advanced-applications.md): Advanced Nimbus BCI patterns for cross-subject training, hybrid systems, adaptive learning, multi-session experiments, and robust deployment. - [Basic Examples](https://docs.nimbusbci.com/examples/basic-examples.md): Compact Nimbus BCI recipes for motor imagery, P300, streaming, calibration, diagnostics, and model comparison. - [Industry Use Cases](https://docs.nimbusbci.com/examples/industry-use-cases.md): Real-world BCI use-case patterns across healthcare, assistive technology, research, and productivity using Nimbus Bayesian models. - [Nimbus BCI Engine](https://docs.nimbusbci.com/index.md): Production-ready Bayesian BCI inference in Python and Julia with real-time streaming, uncertainty quantification, and models for motor imagery, P300, and SSVEP. - [Batch Inference Configuration](https://docs.nimbusbci.com/inference-configuration/batch-processing.md): Configure Nimbus for offline BCI inference, calibration, validation, and research analysis across multiple trials. - [BCI Error Handling](https://docs.nimbusbci.com/inference-configuration/error-handling.md): Handle Nimbus BCI failures with validation checks, confidence thresholds, streaming recovery, and production safeguards. - [BCI Feature Normalization](https://docs.nimbusbci.com/inference-configuration/feature-normalization.md): Normalize BCI features consistently across training, testing, and deployment to improve cross-session reliability. - [EEG Preprocessing Requirements](https://docs.nimbusbci.com/inference-configuration/preprocessing-requirements.md): Prepare EEG features for Nimbus with CSP, bandpower, ERP extraction, and data-quality requirements for reliable BCI inference. - [Real-Time BCI Setup](https://docs.nimbusbci.com/inference-configuration/real-time-setup.md): Set up low-latency BCI acquisition pipelines with LSL, BrainFlow, preprocessing, and streaming handoff to Nimbus SDKs. - [Streaming Inference Configuration](https://docs.nimbusbci.com/inference-configuration/streaming-inference.md): Configure cross-SDK chunk size, temporal aggregation, and streaming decision patterns for low-latency BCI inference. - [Julia SDK API Reference](https://docs.nimbusbci.com/julia-sdk/api-reference.md): Complete API reference for NimbusSDK.jl covering NimbusLDA, NimbusQDA, NimbusProbit, and RxInfer-powered BCI workflows. - [Julia SDK Quickstart](https://docs.nimbusbci.com/julia-sdk/quickstart.md): Get started with NimbusSDK.jl from API key setup to first BCI inference, including installation, authentication, and production-ready basics. - [Julia Streaming Inference](https://docs.nimbusbci.com/julia-sdk/streaming-inference.md): Configure local real-time BCI streaming with NimbusSDK.jl using chunk processing, trial finalization, and confidence-aware aggregation. - [Model Specification](https://docs.nimbusbci.com/model-specification.md): Technical overview of Nimbus probabilistic models, including NimbusLDA, NimbusQDA, NimbusSoftmax, NimbusProbit, and adaptive NimbusSTS. - [NimbusProbit (Julia)](https://docs.nimbusbci.com/models/nimbusprobit.md): NimbusProbit for Julia: Bayesian multinomial probit classification with RxInfer, uncertainty quantification, and flexible non-Gaussian boundaries for BCI tasks. - [Bayesian QDA](https://docs.nimbusbci.com/models/rxgmm.md): Bayesian QDA classifier with class-specific covariances for overlapping BCI classes, delivering uncertainty-aware inference in 15-25ms. - [Bayesian LDA](https://docs.nimbusbci.com/models/rxlda.md): Bayesian LDA with shared covariance for fast motor-imagery BCI inference (10-15ms), including calibrated uncertainty and posterior probabilities. - [NimbusSoftmax (Python)](https://docs.nimbusbci.com/models/rxpolya.md): NimbusSoftmax for Python: Bayesian multinomial logistic regression with Polya-Gamma variational inference for non-Gaussian BCI decision boundaries. - [Bayesian STS](https://docs.nimbusbci.com/models/rxsts.md): Stateful Bayesian STS for non-stationary BCI data, combining drift adaptation, temporal dynamics, and uncertainty-aware inference in 20-30ms. - [Active Learning](https://docs.nimbusbci.com/python-sdk/active-learning.md): Use nimbus-bci active learning helpers to reduce calibration time with pool-based trial ranking, streaming query gates, and label-free stopping. - [Python SDK API Reference](https://docs.nimbusbci.com/python-sdk/api-reference.md): Full nimbus-bci Python API reference for NimbusLDA, NimbusQDA, NimbusSoftmax, NimbusSTS, and core inference utilities. - [Python SDK Installation](https://docs.nimbusbci.com/python-sdk/installation.md): Install and verify the nimbus-bci Python SDK with pip, dependencies, and setup checks for local BCI development. - [Python SDK](https://docs.nimbusbci.com/python-sdk/introduction.md): Overview of the nimbus-bci Python SDK: sklearn-compatible Bayesian classifiers with MNE integration for motor imagery, P300, and SSVEP. - [MNE-Python Integration](https://docs.nimbusbci.com/python-sdk/mne-integration.md): Use nimbus-bci with MNE-Python for complete EEG preprocessing and classification. End-to-end pipeline from raw EEG to BCI predictions. - [Python SDK Quickstart](https://docs.nimbusbci.com/python-sdk/quickstart.md): Build your first nimbus-bci classifier in minutes with installation, sklearn integration, and real-time streaming inference. - [sklearn Integration](https://docs.nimbusbci.com/python-sdk/sklearn-integration.md): Use nimbus-bci with sklearn pipelines, cross-validation, and hyperparameter tuning. Seamless integration with scikit-learn workflows. - [Python Streaming Inference](https://docs.nimbusbci.com/python-sdk/streaming-inference.md): Real-time chunk-by-chunk BCI processing with nimbus-bci. Learn streaming inference, chunk processing, and temporal aggregation for real-time BCI. - [Nimbus Quickstart](https://docs.nimbusbci.com/quickstart.md): Choose the Python or Julia SDK quickstart path and find the setup guide for your first BCI workflow. ## OpenAPI Specs - [openapi](https://docs.nimbusbci.com/api-reference/openapi.json)