gaitmod is a Python library for processing, analyzing, and modeling multi-modal neural and movement data, including LFP, EEG, EMG, and IMU signals. It focuses on real-time gait modulation prediction in Parkinson's disease and supports customizable deep learning pipelines.
It provides tools to:
- Preprocess and clean multi-modal data
- Extract and select features from neural and movement signals
- Perform feature selection and statistical testing
- Train and evaluate machine learning models
- Visualize results
This repository contains code and resources for studying gait modifications using data analysis and machine learning techniques.
Comprehensive documentation is available at Read the Docs.
The latest release of gaitmod can be found on PyPI.
Clone the repository:
git clone https://github.com/yourusername/gaitmod.git
cd gaitmodInstall dependencies:
pip install -r requirements.txtRun the main analysis script:
python main.pyRefer to the documentation on Read the Docs for detailed usage instructions.
gaitmod/
├── data/
├── src/
├── results/
├── README.md
└── requirements.txt
Contributions are welcome! Please open issues or submit pull requests.
This project is licensed under the MIT License.