OpenMuse

OpenMuse

The Open Python Muse S Athena EEG Decoder

This software allows recording, streaming via LSL and visualizing signals from the Muse S Athena headband.

[!WARNING]
OpenMuse is NOT an official product of InteraXon Inc and is not affiliated with or endorsed by the company. It does not come with any warranty and should be considered an experimental software developed for research purposes only.

Roadmap

• Record raw data to a file
• Decode raw data into a pandas DataFrame
• Stream data over LSL
• Visualise live LSL streams
• Stream in battery signal and display battery level in visualizer
• Consider adding a visualization of the PSD of each signal
• Validate timestamping accuracy experimentally: confirm we get time-locked ERPs
• Validate the best PPG signal extraction method
• Validate fNIRS signal extraction method

Installation

Install from GitHub by opening a terminal and running:

pip install https://github.com/DominiqueMakowski/OpenMuse/zipball/main

Usage

After installing the package, open a terminal and use the following commands:

Find Muse devices

Power up the Muse S Athena headband (a blue light should appear on the front) and run:

OpenMuse find

This will print the MAC addresses of nearby Muse devices. Note the address of your device for the next steps.

Record data to a file

OpenMuse record --address <your-muse-address> --duration 60 --outfile data.txt

Loading Recorded Data

Once your file is recorded, you can load it in Python using:

import pandas as pd
import OpenMuse 

with open("data.txt", "r", encoding="utf-8") as f:
    messages = f.readlines()
data = OpenMuse.decode_rawdata(messages)

# Plot Movement Data
data["ACCGYRO"].plot(
    x="time",
    y=["ACC_X", "ACC_Y", "ACC_Z", "GYRO_X", "GYRO_Y", "GYRO_Z"],
    subplots=True
)

Lab Streaming Layer (LSL)

[!NOTE]
OpenMuse uses MNE-LSL, an improved python-binding for the Lab Streaming Layer C++ library, mne_lsl.lsl, replacing pylsl.

To stream data over LSL, use:

OpenMuse stream --address <your-muse-address>

This creates separate LSL streams for groups of channels (Muse_EEG, Muse_ACCGYRO, etc.).

The following options are available:

• --address: The MAC address of your Muse device (required)
• --duration: Duration of the recording in seconds (if not specified, streaming will continue until you press Ctrl+C in the terminal)
• --preset: Preset configuration (default: p1041 for all channels)
• --record: Record raw BLE packets to a file (optional)

Visualize Live Streams

To visualize live LSL streams, open a new terminal (while the streaming is running) and run:

OpenMuse view

Hyperscanning (Multiple Devices Streaming)

To record from multiple Muse devices simultaneously (e.g., for hyperscanning studies):

1. Discover devices: Power on all Muse headbands and run OpenMuse find to note down each device’s MAC address.

2. Start streaming: You can stream from multiple devices simultaneously by passing multiple MAC addresses:

OpenMuse stream --address 00:55:DA:B9:FA:20 00:55:DA:BB:CD:CD

This will manage all connections in parallel within a single process. LSL streams will be named with their respective device IDs (e.g., Muse-EEG (MAC1)), allowing you to distinguish them on the receiving end.

3. Visualize (optional): When multiple devices are streaming, the viewer will automatically detect and display streams from only the first device by default to avoid confusion. To view a specific device, use the --address filter:

# View first device
OpenMuse view --address 00:55:DA:B9:FA:20

# View second device (open in a separate terminal)
OpenMuse view --address 00:55:DA:BB:CD:CD

4. Record with LabRecorder: Use LabRecorder to record all streams to an XDF file. Select all the streams from your devices (e.g., Muse-EEG (00:55:DA:B9:FA:20), Muse-EEG (00:55:DA:BB:CD:CD), etc.).

Multi-Device Raw Recording (Alternative to LSL)

You can also do raw BLE packet recordings from multiple Muse devices simultaneously by passing multiple MAC addresses:

OpenMuse record --address 00:55:DA:B9:FA:20 00:55:DA:BB:CD:CD --duration 60 --outfile data.txt

When recording from multiple devices, the device address will be automatically appended to the filename to avoid collisions (e.g., data_0055DAB9FA20.txt, data_0055DABBCDCD.txt).

Technical Details

Specs

Muse S Athena specs (From the Muse website):

• Wireless Connection: BLE 5.3, 2.4 GHz
• EEG Channels: 4 EEG channels (TP9, AF7, AF8, TP10) + 4 amplified Aux channels
  • Sample Rate: 256 Hz
  • Sample Resolution: 14 bits / sample
• Accelerometer: Three-axis at 52Hz, 16-bit resolution, range +/- 2G
• Gyroscope: Three-axis at 52Hz, 16-bit resolution, range +/- 250dps
• PPG Sensor: Triple wavelength: IR (850nm), Near-IR (730nm), Red (660nm), 64 Hz sample rate, 20-bit resolution
• fNIRS Sensor: 5-optode bilateral frontal cortex hemodynamics, 64 Hz sample rate, 20-bit resolution

Presets

Presets	EEG	Optics	ACC/GYRO	Battery	Red LED
p20, p21, p50, p51, p60, p61	EEG4	—	X	X	off
p1034, p1043	EEG8	Optics8	X	X	bright
p1044	EEG8	Optics8	X	X	dim
p1035	EEG4	Optics4	X	X	dim
p1041, p1042	EEG8	Optics16	X	X	bright
p1045	EEG8	Optics4	X	X	dim
p1046	EEG8	Optics4	X	X	—
p4129	EEG8	Optics4	X	X	dim

Table derived from the signature of the data packets present in the data. More presets could exist.

Acknowledgements

This project would not have been possible without the breakthroughs of amused-Py that identified the communication protocol and AbosaSzakal’s parser who documented the data structure.