🧁 Baking_EEG

Your Recipe for EEG Insights!

Baking_EEG is a comprehensive, modular, and open-source Python toolkit for the analysis of EEG signals, with a special focus on evoked potential protocols. Designed for both research and clinical applications, it supports a wide range of acquisition systems and provides robust pipelines for preprocessing, decoding, statistics, and visualization.

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Key Features

• Multi-protocol support: Analyze EEG data from various evoked potential paradigms
• Multi-system compatibility: Works with BrainAmp, EGI, Micromed, and more
• Flexible pipelines: Individual and group-level analyses, including ERP, temporal decoding and temporal generalization matrix
• Statistical analysis: Intra- and inter-subject statistics, permutation tests, FDR, cluster-based correction
• Rich visualization: Automated dashboards and publication-ready plots
• Reproducibility: Configurable, version-controlled, and ready for cluster computing (SLURM/submitit)
• Extensible: Modular codebase for easy adaptation to new protocols or analysis needs

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📂 Project Structure

Baking_EEG/
├── Baking_EEG/                # Core analysis modules (preprocessing, decoding, stats, etc.)
├── base/                      # Base decoding and pipeline utilities
├── config/                    # Configuration files (protocols, classifiers, etc.)
├── examples/                  # Example scripts and analysis workflows
├── results/                   # Output results (organized by protocol, subject, etc.)
├── submitit/                  # SLURM/submitit job submission scripts
├── utils/                     # Utility functions (visualization, loading, etc.)
├── requirements.txt           # Main dependencies
├── README.md                  # This file
└── ...

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🛠️ Installation

Prerequisites

• Python 3.11 or higher recommended
• See requirements.txt for all dependencies

Quick Start

1. Clone the repository:

   git clone https://github.com/Lx37/Baking_EEG.git
   cd Baking_EEG

2. (Recommended) Create a virtual environment:

   python3 -m venv bakingeeg_env
   source bakingeeg_env/bin/activate

   Or use uv for faster installs:

   uv venv bakingeeg_env --python 3.12
   source bakingeeg_env/bin/activate
   uv pip install -r requirements.txt

3. Install dependencies:

   pip install -r requirements.txt

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Usage

1. Prepare your EEG data

• Organize your raw/preprocessed EEG files according to your acquisition system and protocol.
• Update or check the configuration files in config/ as needed.

2. Run an analysis

• Single subject decoding:

  python examples/run_decoding_one_lg.py --subject_id <SUBJECT_ID>

• Group analysis (SLURM/submitit):

  python submitit/submit_1group_lg_all.py

• Custom analysis: Explore scripts in examples/ or build your own using the modular functions.

3. Review results

• Results (metrics, plots, logs) are saved in the results directory, organized by protocol and subject.
• Use the visualization utilities in utils/ for further exploration.

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🧩 Extending Baking_EEG

• Add new protocols by editing or adding config files in config/
• Implement new analysis pipelines in Baking_EEG or base/
• Contribute new visualization or statistics modules in utils/

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🤝 Contributing

Contributions are welcome! Please:

• Fork the repository and create a feature branch
• Submit pull requests with clear descriptions
• Report bugs or request features via GitHub Issues

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📚 Documentation

• Examples and scripts are available in the examples/ folder.

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📝 License

This project is licensed under the BSD 3-Clause License. See the LICENSE file for details.

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Contributors

• CNRS, Alexandra Corneyllie, Tom Balay, and all contributors
• Inspired by the open-source neuroscience and Python communities

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Baking_EEG: Turning raw EEG into scientific delicacies!

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