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