Metadata-Version: 2.4
Name: biosynth-emg
Version: 0.1.0
Summary: Physics-informed synthetic EMG signal generator for prosthetic control
Home-page: https://github.com/janxhg/BioSynth-EMG
Author: NetechAI
Author-email: joaquinsturzt26@gmail.com
Project-URL: Bug Reports, https://github.com/janxhg/BioSynth-EMG/issues
Project-URL: Source, https://github.com/janxhg/BioSynth-EMG
Project-URL: Paper, https://github.com/janxhg/BioSynth-EMG/blob/main/paper.md
Keywords: emg,electromyography,synthetic-data,prosthetics,machine-learning,biomedical
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.21.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: h5py>=3.1.0
Requires-Dist: matplotlib>=3.4.0
Requires-Dist: torch>=1.9.0
Requires-Dist: scikit-learn>=0.24.0
Requires-Dist: pandas>=1.3.0
Requires-Dist: tqdm>=4.62.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license-file
Dynamic: project-url
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# BioSynth-EMG: Physics-Informed Generative Framework for Synthetic Myoelectric Signals

A physics-based synthetic EMG signal generator for training low-latency neural networks in prosthetic control systems.

## Features

- **Biomechanical Layer**: Motor unit firing rate simulation using Poisson distribution
- **Electrical Propagation Layer**: MUAP generation with Hermite functions
- **Realistic Noise**: Gaussian noise, power line interference, and movement artifacts
- **High Performance**: Optimized for GTX 1650 (<1ms generation for 1s signal)
- **Dataset Generation**: 8-channel EMG with gesture labels and force values

## Installation

```bash
pip install -r requirements.txt
```

## Usage

```python
from biosynth_emg import BioSynthGenerator

generator = BioSynthGenerator()
data = generator.generate_dataset(num_samples=1000, duration=1.0)
```

## Paper

Based on "BioSynth-EMG: A Physics-Informed Generative Framework for Synthetic Myoelectric Signal Synthesis and Prothetic Control Benchmarking"
