EMG Capture and Visualization System

A complete Electromyography (EMG) acquisition and monitoring system featuring custom hardware design, high-speed signal conditioning, microcontroller firmware, and real-time visualization tools.

🚀 Project Overview

This project provides an end-to-end solution for capturing and analyzing EMG signals. It includes:

• Analog Front-End: A custom PCB designed with INA333 instrumentation amplifiers and LMP770x op-amps for precise signal conditioning.
• High-Speed Acquisition: An Adafruit M0 (SAMD21) microcontroller sampling at 25 kSPS (40µs interval).
• Wireless Transmission: Real-time data streaming via UDP over WiFi.
• Software Suite: A Python-based PC application for real-time visualization, digital filtering (Notch, Bandpass), and data recording.

📂 Directory Structure

e:\Projects\EMG-Capture-and-Visualization\
├── 01 Simulations/       # Circuit simulations and Python signal processing demos
│   ├── LT Spice/         # LTSpice models (INA333, etc.) and behavioral simulations
│   └── Dataset/          # Test datasets
├── 02 Hardware/          # Hardware design files
│   └── PCB/              # Altium Designer Schematics (.SchDoc) and PCB Layouts (.PcbDoc)
├── 03 firmware/          # Microcontroller code and PC software
│   ├── adafruitM0/       # Arduino sketch for the Adafruit M0 Feather
│   └── Python/           # Real-time visualization application (PyQt6)
│       ├── app.py        # Main GUI Application
│       └── EMG_game.py   # Flappy Bird Game
├── 04 3D Design/         # 3D models for enclosures and mechanical parts
└── 05 media/             # Project images and demos

🌟 Key Features

Hardware

• Precision Amplification: Uses INA333 for low-noise instrumentation amplification.
• Signal Conditioning: Analog filtering and gain stages tailored for EMG signals.
• Custom PCB: Professionally designed using Altium Designer.
• Wearable Design:

Left: Final PCB Design | Right: Device attached to arm for data capture

Firmware (Adafruit M0)

• High Sampling Rate: Optimized ADC register settings for 25kHz sampling.
• Efficient Transmission: Packs 256 samples into binary UDP packets for low-latency wireless streaming.
• Differential Reading: Performs on-chip differential ADC readings (A1 - A2).

Software (Python App)

The system includes a powerful Python-based GUI (app.py) for real-time analysis.

• Real-Time Oscilloscope: Smooth visualization using PyQtGraph and PyQt6.
• Digital Signal Processing:
  • Notch Filter: Removes 50Hz/100Hz/150Hz mains hum.
  • Bandpass Filter: Configurable Butterworth filter (e.g., 25Hz - 150Hz).
• Data Recording: Save captured sessions to CSV for offline analysis.

Real-time EMG data visualization in the GUI

🎮 Flappy Bird Game (HCI Demo)

To demonstrate Human-Computer Interaction (HCI) capabilities, the project includes a game controlled by muscle activity (EMG_game.py).

• Control Scheme: Muscle contractions (EMG signal crossing a threshold) trigger the bird to jump.
• Visual Feedback: Real-time EMG signal envelope is displayed at the bottom of the game window.
• Engaging Interaction: A fun and interactive way to visualize bio-potential signals and their control applications.

Flappy Bird game controlled by EMG signals

🎥 Demo

Check out the system in action:

demo.mp4

🛠️ Getting Started

Prerequisites

• Hardware: Assembled PCB, Adafruit M0 Feather (WiFi).
• Software: Python 3.x, Arduino IDE.

1. Firmware Setup

1. Open 03 firmware/adafruitM0/adafruitM0.ino in Arduino IDE.
2. Install the WiFi101 library.
3. Update the ssid, pass, and remoteIp (your PC's IP address) in the code.
4. Upload to the Adafruit M0 board.

2. Python Environment

Install the required Python libraries:

pip install numpy scipy pyqt6 pyqtgraph pygame

3. Running the Visualization

Navigate to the Python directory and run the application:

cd "03 firmware/Python"
python app.py

1. Enter the Port (default: 8888).
2. Click CONNECT to start streaming.
3. Use the checkboxes to enable Mains Hum removal or Bandpass filtering.
4. Click START CAPTURE to record data to a CSV file.

4. Playing the Game

To try the game demo:

cd "03 firmware/Python"
python EMG_game.py

• Flex your muscle to make the bird jump!
• Adjust gain with Up/Down arrow keys if needed.

📊 Simulation

The 01 Simulations folder contains:

• LTSpice: Run INA_behavioral.asc to simulate the analog front-end behavior.
• Python Demo: Run GUI.py in 01 Simulations/LT Spice/ to see a simulation of noise addition and filtering on pre-recorded data.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.