EEG LabStreamingLayer (LSL) EEG BCI LSL

Wednesday, 12 de May de 2021

LabStreamingLayer (LSL) EEG BCI LSL

What is LSL?

 

Lab Streaming Layer (LSL) is a software solution that enables the unified and synchronized collection of data streams form different sources. The source can be something as simple as a mouse, a keyboard, or a microphone but can be complex as an EEG signal, a fNIRS signal, or a stream of event markers coming from a presentation software.

 

 

This means that with LSL you can easily perform hyperscanning (i.e., two or more simultaneous EEG measurement from different participants) and multi-measure studies (i.e., EEG-fNIRS), without having to worrying about synchronization issues or sharing hardware triggers. In fact, LSL will store all the data (i.e., EEG stream, fNIRS stream, trigger stream) in the same file (.xdf format).

 

How do I use LSL with Brain Products?

 

To connect a Brain Products amplifier to LSL you need to download a connector form GitHub:

 

 

The apps are intuitive to use. You just check the parameters and hit LINK. Then you can open the BrainVision LSL Viewer and monitor the signal (https://pressrelease.brainproducts.com/lsl-viewer/). To record the data instead you need this app called LabRecorder (https://github.com/labstreaminglayer/App-LabRecorder/releases/tag/v1.14.0). It can see streams from different sources, as long as all the computers from which the streams are connected to the same WIFI or to the same LAN. It saves data into .xdf format. You can convert them into .eeg via EEGLAB/MNELAB.

 

Want more information?

 

Please check the resources from Brain Products. For an introduction to the LSL connectors and the Brain Products hardware (https://pressrelease.brainproducts.com/lsl-github/). For a detailed guide on how to set-up an LSL acquisition session with Brain Products hardware (https://bci.plus/data-processing-with-lsl-bv/). For an example of multimodal set-up (EEG-fNIRS) with LSL (https://pressrelease.brainproducts.com/eeg-fnirs-setup/).

The content published here is the exclusive responsibility of the authors.

Autor:

Jackson Cionek

#eegerpbcifftp300