https://github.com/neuro-mechatronics-interfaces/python_intan

Tools and demos for working with EMG data from intan using python
https://github.com/neuro-mechatronics-interfaces/python_intan

circuitpython cuda emg pico python realtime tensorflow

Last synced: 5 months ago
JSON representation

Tools and demos for working with EMG data from intan using python

• Host: GitHub
• URL: https://github.com/neuro-mechatronics-interfaces/python_intan
• Owner: Neuro-Mechatronics-Interfaces
• License: mit
• Created: 2024-10-14T18:59:42.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-01-07T16:54:33.000Z (6 months ago)
• Last Synced: 2025-02-19T23:41:39.961Z (5 months ago)
• Topics: circuitpython, cuda, emg, pico, python, realtime, tensorflow
• Language: Python
• Homepage:
• Size: 10.7 MB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Python Intan

This repository provides a set of tools and demonstrations for working with electromyography (EMG) data collected using Intan systems. It includes scripts for data preprocessing, feature extraction, machine learning (ML) model training, and real-time classification. These tools are designed to facilitate gesture recognition from sEMG signals, which can be applied in prosthetics, robotics, and neuromuscular research.

![intan_logo.png](/assets/intan_logo.png)

Code was written and tested using Windows 11, Python 3.10.

![Python](https://img.shields.io/badge/python-3.10-blue)

![License](https://img.shields.io/badge/license-MIT-green)

## Repository Structure

* `3D_printed_arm_control` - Hardware and software resources for robot arm control using microcontroller supporting CircuitPython.

* `realtime_decoder` - Perform inference on an EMG signal in real-time using a trained model.

* `gesture_classifier` - Scripts for training and testing machine learning models for gesture classification.

* `utilities` - Helper functions for data preprocessing, feature extraction, and model evaluation.

## Installation

1. It is recommended to use a virtual environment to manage dependencies. To create a new virtual environment with [anaconda](https://www.anaconda.com/products/individual), use the following command:

   ```bash

   conda create -n intan python=3.10

   conda activate intan

   ```

2. Download the repository using git:

   ```bash

   git clone https://github.com/Neuro-Mechatronics-Interfaces/Python_Intan.git

   cd Intan_EMG_Python

   ```

3. To install dependencies, use the provided requirements file:

   ```bash

   pip install -r requirements.txt

   ```

   

## Demo

A demo script in the main directory shows a quick example of opening and plotting EMG waveforms from a .rhd file. Run the following command:

```bash

python load_rhd_demo.py

```

   

## Future Improvements

 - [x] Add support for other classifiers

 - [x] Expand feature extraction to support CNN architectures.

 - [x] Add support for real-time classification using the trained models.

 - [x] Integrate with the Intan RHX system via TCP for real-time data streaming.

 - [x] Integrate support for sending serial commands to operate robot arm in realtime. 

 - [ ] Refine realtime classification to include a GUI for visualizing the data.

 - [ ] Allow downloading of dataset to perform actual training and testing.

 - [ ] Allow data analysis methods to be used on the dataset.