Muse Real-Time Brainwave Visualizer

A lightweight, high-performance Python interface for the Interaxon Muse headband. This tool connects via Bluetooth Low Energy (BLE), processes raw EEG data in real-time using the Welch method, and visualizes the five major brainwave bands (Delta, Theta, Alpha, Beta, Gamma).

Unlike complex BCI suites, this project focuses on a "plug-and-play" experience for researchers and developers who need immediate access to live brainwave data and CSV logging.

Features

• Real-time Visualization: Smooth, multi-threaded plotting of band power using pyqtgraph.
• Automatic Device Discovery: Includes a helper script to find your Muse's MAC address instantly.
• Signal Processing: Implements a sliding buffer and Welch's Power Spectral Density (PSD) to extract frequency bands.
• Data Logging: Automatically saves session data (Timestamp + Band Powers) to the data/ folder for post-analysis.
• Stable Connection: Uses a robust proxy to handle Bluetooth dropouts and reconnections.

Project Structure

Muse-Realtime-Monitor/
├── data/                  # Stores your recorded CSV session files
├── proxies/               # Core connection logic for the Muse headband
│   └── MuseProxy.py       
├── find_muse.py           # Helper script to scan for your device
├── main.py                # Main application script
├── requirements.txt       # List of dependencies
├── LICENSE                # MIT License
└── README.md              # Documentation

Prerequisites

• Hardware: A Muse Headband (Muse 2 or Muse S recommended).
• OS: Windows, macOS, or Linux with Bluetooth Low Energy (BLE) support.
• Python: Version 3.8 or higher.

Installation

1. Clone the repository:

git clone https://github.com/YOUR_USERNAME/Muse-Realtime-Monitor.git
cd Muse-Realtime-Monitor

2. Install dependencies:

pip install -r requirements.txt

Configuration & Usage

Step 1: Find your Device

If you don't know your Muse's MAC address, turn on your headband and run the helper script:

python find_muse.py

Copy the address it finds (e.g., 00:55:DA:B9:9C:EE).

Step 2: Run the Monitor

Open main.py and replace the MUSE_MAC_ADDRESS variable at the top with your address.

Alternatively, you can pass the address directly as an argument:

python main.py 00:55:DA:B9:9C:EE

Step 3: Controls

• The connection process will start automatically.
• Once connected, a window will appear displaying the 5 live brainwave bands.
• To Stop: Close the visualization window or press Ctrl+C in the terminal.
• Data: Your session is automatically saved to data/brainwaves_YYYY-MM-DD_...csv.

The Frequency Bands

This tool visualizes the following standard EEG bands:

Band	Frequency Range	Associated State
Delta	0.5 - 4 Hz	Deep sleep, unconsciousness
Theta	4 - 8 Hz	Meditation, drowsiness, creativity
Alpha	8 - 13 Hz	Relaxation, closed eyes, calm
Beta	13 - 30 Hz	Active thinking, focus, alertness
Gamma	30 - 100 Hz	High-level cognitive processing

Contributing

Contributions are welcome! If you have a bug fix or a new feature (like Neurofeedback audio), please fork the repository and submit a pull request.

Disclaimer

This software is for educational and research purposes only. It is not a medical device and should not be used for diagnosis, treatment, or prevention of any health condition.

Acknowledgements

Author: Rubathiyagish (rubathiyagish@gmail.com)

Mentor/Guide: Dr.Sandeep Budde (sandeepvarmabudde@gmail.com)

Institution: NITTE School of Architecture Planning and Design, Bengaluru

License

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