Recording Brainwaves with a Raspberry Pi

Streaming, saving and scripting real EEG brainwave data can be done on a Raspberry Pi. In this example we will be using the Muse (2016) EEG headband combined with a Raspberry Pi 3 B+ inside a PiTop. The tools being used have been available for  years making this a straight forward process to get up and running. Let’s start with getting our Pi running a Raspbian based release up to date. Open a terminal window and start running the following commands.

$ sudo apt-get update
$ sudo apt-get -y upgrade
$ sudo apt-get -y dist-upgrade

Next we need to clone the Muse-LSL library from github. This “lab streaming layer” is popular and being used for many different types of EEG headsets. It does include connection and recording script examples which are specific to the Muse (2016) headset. We will come back to this and run the example scripts after we take care of the dependencies.

$ git clone https://github.com/alexandrebarachant/muse-lsl.git

We must install the following python packages for muse-lsl to work:

$ pip install git+https://github.com/peplin/pygatt
$ pip install pylsl
$ pip install scikit-learn

Finally, we have one bit of craziness to deal with. Using a Raspberry Pi means we are on an ARM based processor. The default distribution for liblsl includes a 32-bit i386 architecture library which will stop us in our tracks. We need to download an updated library bundle, unzip it and copy the arm based lsl library over the i386 one.

$ wget ftp://sccn.ucsd.edu/pub/software/LSL/SDK/liblsl-C-C++-1.11.zip
$ unzip liblsl-C-C++-1.11.zip
$ sudo cp liblsl-bcm2708.so /usr/local/lib/python2.7/dist-packages/pylsl/liblsl32.so

Okay, now we are setup to catch some brainwaves. Let’s start by power up the Muse headband and scanning it for it using the muse-lsl scanner code:

$ cd muse-lsl
$ python scan_devices.py

We will get a device name and a MAC address if all goes well. Let’s stick with the mac address and create a connection from the Raspberry Pi to the Muse headband. We are connected and streaming data.

$ python muse-lsl.py –address 00:55:DA:B0:9F:A3

We are connected. Now that EEG data can flow towards us we have three options. We can view it, record it or stream it. Here is how we can view the data using the lsl-viewer code included with muse-lsl. This example script shows which sensors are receiving data and the values coming in.

$ python lsl-viewer.py

The real power of the Raspberry Pi is that it can be tucked away and left to do tasks like recording EEG automatically. Let’s finish this with a look at what the command line recording syntax and resulting data file look like. We can hit ^C at any point and get a dump.csv file. The first five values are the different electrode values and the final value is a timestamp.

$ python muse-record.py –address 00:55:DA:B0:9F:A3


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