autocleaneeg-pipeline 3.0.0

A modular framework for automated EEG data processing, built on MNE-Python

AutoCleanEEG Pipeline

A modular framework for automated EEG data processing, built on MNE‑Python.

Features

• Framework for automated EEG preprocessing with "lego block" modularity
• Support for multiple EEG paradigms (ASSR, Chirp, MMN, Resting State)
• BIDS-compatible data organization and comprehensive quality control
• Extensible plugin system for file formats, montages, and event processing
• Research-focused workflow: single file testing → parameter tuning → batch processing
• Detailed output: BIDS‑compatible derivatives, single task log file, stage files, exports, and QA visualizations

Installation (uv)

Use Astral's uv for fast, isolated installs. If you don't have uv yet, see https://docs.astral.sh/uv/

• Install CLI (recommended for users):

uv tool install autocleaneeg-pipeline
autocleaneeg-pipeline --help

• Upgrade or remove:

uv tool upgrade autocleaneeg-pipeline
uv tool uninstall autocleaneeg-pipeline

• Development install from source (editable install):

git clone https://github.com/cincibrainlab/autocleaneeg_pipeline.git
cd autocleaneeg_pipeline
uv tool install -e --upgrade . --force
autocleaneeg-pipeline --help # Slow on first run!

Documentation

Full documentation is available at https://docs.autocleaneeg.org

Development

For contributors, we provide a Makefile with convenient development commands:

make help          # Show all available commands
make check         # Run code quality checks
make format        # Auto-format code
make lint          # Run linting and type checking
make test          # Run unit tests
make test-cov      # Run tests with coverage
make ci-check      # Run CI-equivalent checks locally

See CONTRIBUTING.md for full development guidelines.

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Cincinnati Children's Hospital Research Foundation
• Built with MNE-Python