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. For branch workflow and cleanup expectations, see BRANCHING.md.
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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