A Python package for streaming, visualizing, and recording EEG data from the Muse devices developed by InteraXon.
The code relies on a number of different bluetooth backends for connecting to the muse. We recommend using the bleak backend (enabled by default), but you may be interested in BlueMuse for a GUI to discover and connect to Muse devices on Windows or [bgapi] if you are on a Mac with a BLED112 dongle.
Compatible with Python 2.7 and Python 3.x
Compatible with Muse 2, Muse S, and the classic Muse (2016)
Note: if you run into any issues, first check out out Common Issues and then the Issues section of this repository
Install Muse LSL with pip
pip install muselsl
On Windows 10, we recommend using the BlueMuse GUI to set up an LSL stream. On Mac and Linux, the easiest way to get Muse data is to use Muse LSL directly from the command line. Use the -h flag to get a comprehensive list of all commands and options.
To print a list of available muses:
$ muselsl list
To begin an LSL stream from the first available Muse:
$ muselsl stream
To connect to a specific Muse you can pass the name of the device as an argument. Device names can be found on the inside of the left earpiece (e.g. Muse-41D2):
$ muselsl stream --name YOUR_DEVICE_NAME
You can also directly pass the MAC address of your Muse. This provides the benefit of bypassing the device discovery step and can make connecting to devices quicker and more reliable:
$ muselsl stream --address YOUR_DEVICE_ADDRESS
Once an LSL stream is created, you have access to the following commands.
Note: the process running the stream command must be kept alive in order to maintain the LSL stream. These following commands should be run in another terminal or second process
To view data:
$ muselsl view
If the visualization freezes or is laggy, you can also try the alternate version 2 of the viewer. Note: this will require the additional vispy and mne dependencies
$ muselsl view --version 2
To record EEG data into a CSV:
$ muselsl record --duration 60
Note: this command will also save data from any LSL stream containing 'Markers' data, such as from the stimulus presentation scripts in EEG Notebooks
Alternatively, you can record data directly without using LSL through the following command:
$ muselsl record_direct --duration 60
Note: direct recording does not allow 'Markers' data to be recorded
Muse LSL was designed so that the Muse could be used to run a number of classic EEG experiments, including the P300 event-related potential and the SSVEP and SSAEP evoked potentials.
The code to perform these experiments is still available, but is now maintained in the EEG Notebooks repository by the NeuroTechX community.
If you want to integrate Muse LSL into your own Python project, you can import and use its functions as you would any Python library. Examples are available in the examples folder:
from muselsl import stream, list_muses
muses = list_muses()
stream(muses[0]['address'])
# Note: Streaming is synchronous, so code here will not execute until after the stream has been closed
print('Stream has ended')In addition to EEG, the Muse also provides data from an accelerometer, gyroscope, and, in the case of the Muse 2, a photoplethysmography (PPG) sensor. These data types can be enabled via command line arguments or by passing the correct parameters to the stream function. Once enabled, PPG, accelerometer, and gyroscope data will streamed in their own separate LSL streams named "PPG", "ACC", and "GYRO", respectively.
To stream data from all sensors in a Muse 2 from the command line:
muselsl stream --ppg --acc --gyro
As a library function:
from muselsl import stream, list_muses
muses = list_muses()
stream(muses[0]['address'], ppg_enabled=True, acc_enabled=True, gyro_enabled=True)To record data from an alternate data source:
muselsl record --type ACC
Note: The record process will only record from one data type at a time. However, multiple terminals or processes can be used to record from multiple data types simultaneously
Lab Streaming Layer or LSL is a system designed to unify the collection of time series data for research experiments. It has become standard in the field of EEG-based brain-computer interfaces for its ability to make seperate streams of data available on a network with time synchronization and near real-time access. For more information, check out this lecture from Modern Brain-Computer Interface Design or the LSL repository
- Connection issues with BLED112 dongle:
- You may need to use the
--interfaceargument to provide the appropriate COM port value for the BLED112 device. The default value is COM9. To setup or view the device's COM port go to your OS's system settings
pygatt.exceptions.BLEError: Unexpected error when scanning: Set scan parameters failed: Operation not permitted(Linux)
- This is an issue with pygatt requiring root privileges to run a scan. Make sure you have
libcapinstalled and runsudo setcap 'cap_net_raw,cap_net_admin+eip' `which hcitool`
pygatt.exceptions.BLEError: No characteristic found matching 273e0003-4c4d-454d-96be-f03bac821358(Linux)
- There is a problem with the most recent version of pygatt. Work around this by downgrading to 3.1.1:
pip install pygatt==3.1.1
pygatt.exceptions.BLEError: No BLE adapter found(Linux)
- Make sure your computer's Bluetooth is turned on.
pygatt.exceptions.BLEError: Unexpected error when scanning: Set scan parameters failed: Connection timed out(Linux)
- This seems to be due to a OS-level Bluetooth crash. Try turning your computer's bluetooth off and on again
'RuntimeError: could not create stream outlet'(Linux)
- This appears to be due to Linux-specific issues with the newest version of pylsl. Ensure that you have pylsl 1.10.5 installed in the environment in which you are trying to run Muse LSL
- If this is preceded by
Could not instantiate IPv4 stack: getrandom, it could be this issue which can be resolved by buildingliblslwith-DBOOST_UUID_RANDOM_PROVIDER_FORCE_POSIX(e.g. by editingstandalone_compilation_linux.sh)
@misc{muse-lsl,
author = {Alexandre Barachant and
Dano Morrison and
Hubert Banville and
Jason Kowaleski and
Uri Shaked and
Sylvain Chevallier and
Juan Jesús Torre Tresols},
title = {muse-lsl},
month = may,
year = 2019,
doi = {10.5281/zenodo.3228861},
url = {https://doi.org/10.5281/zenodo.3228861}
}
Alexandre Barachant, Dano Morrison, Hubert Banville, Jason Kowaleski, Uri Shaked, Sylvain Chevallier, & Juan Jesús Torre Tresols. (2019, May 25). muse-lsl (Version v2.0.2). Zenodo. http://doi.org/10.5281/zenodo.3228861