gaitmod 0.1.0

real-time gait modulation prediction using multimodal neural and movement data (LFP, EEG, IMU, EMG) — designed for closed-loop DBS systems in Parkinson's diseas.

GaitMod

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
• Train and evaluate machine learning models
• Visualize results and model performance

Table of Contents

• GaitMod
  • Table of Contents
  • Overview
  • Documentation
  • Installation
  • Usage
  • Project Structure
  • Contributing
  • License

Overview

This repository contains code and resources for studying gait modifications using data analysis and machine learning techniques.

Documentation

Comprehensive documentation is available at https://gaitmod.readthedocs.io/.

Installation

Clone the repository:

git clone https://github.com/yourusername/gaitmod.git
cd gaitmod

Install dependencies:

pip install -r requirements.txt

Usage

Run the main analysis script:

python main.py

Refer to the documentation on Read the Docs for detailed usage instructions.

Project Structure

gaitmod/
├── data/
├── src/
├── results/
├── README.md
└── requirements.txt

Contributing

Contributions are welcome! Please open issues or submit pull requests.

License

This project is licensed under the MIT License.