MEGaNorm

MEGaNorm

Normative modeling of EEG/MEG brain dynamics across populations and timescales.

MEGaNorm is a Python package that wraps MNE-Python and PCNToolkit functionalities for extracting functional imaging-derived phenotypes (f-IDPs) from large-scale EEG and MEG datasets, and then deriving their normative ranges. It allows researchers to analyze large MEG and EEG dataset using high-performance computing facilities, and then build, visualize, and analyze normative models of brain dynamics across individuals.

🚀 Features

• Compatibility with MNE-Python, PCNToolkit, SpecParam libraries
• Normative modeling of oscillatory brain activity
• Using high performance capabilities on SLURM clusters
• EEG and MEG support with BIDS integration
• Ready for reproducible deployment with Docker

📦 Installation

Option 1: From PyPI (recommended)

This is the easiest way to get started if you just want to use the toolbox.

conda create --channel=conda-forge --strict-channel-priority --name mne python=3.12 mne

conda activate mne

pip install meganorm

Option 2: Installl from the source files

# 1. Create and activate environment
conda create --channel=conda-forge --strict-channel-priority --name mne python=3.12 mne

conda activate mne

# 2. Clone and install MEGaNorm
git clone https://github.com/ML4PNP/MEGaNorm.git
cd MEGaNorm/
pip install .

Option 3: Using Docker

We provide a pre-configured Docker environment with Jupyter Lab. You can either build the image locally or pull the latest version from Docker Hub.

Option A: Build the image locally

make build
make run

Option B: Pull the latest image from Docker Hub

make pull
make run

This mounts:

• notebooks/ → for saving Jupyter notebooks
• results/ → for analysis outputs
• data/ → for raw/processed EEG/MEG data

Jupyter will open in your browser on http://localhost:8888

📒 Getting Started (under construction)

👉 documentation Early helpers are available at: https://meganorm.readthedocs.io. We are working hard to add more thorough documentations and tutorials and they will be available soon.

import meganorm

Explore examples in the notebooks/ folder.

🧚‍♂️ Testing (under construction)

Run unit tests using:

pytest tests/

🧠 Citing MEGaNorm

If you use MEGaNorm in your work, please cite:

DOI: 10.5281/zenodo.15441320

You can download BibTeX and other citation formats directly from the Zenodo page.

🤝 Contributing

Contributions, issues and feature requests are welcome!

See CONTRIBUTING.md for more info.

📜 License

This project is licensed under the terms of the GNU General Public License v3.0 – see the LICENSE file for details.