Summary

The lab streaming layer (LSL) is a system for the unified collection of measurement time series in research experiments that handles both the networking, time-synchronization, (near-) real-time access as well as optionally the centralized collection, viewing and disk recording of the data.

Quick Start

The most common way to use LSL is to use one or more applications with integrated LSL functionality.

• Take a look at the list of supported devices and tools and follow the instructions to start streaming data from your device. If your device is not in the list then see the "Getting Help" section below.
• Download LabRecorder from its release page. (Note that LabRecorder saves data to Extensible Data Format (xdf) which has its own set of tools for loading data after finishing recording.)
• Use LSL from your scientific computing environment. LSL has many language interfaces, including Python and Matlab. Python users need only pip install pylsl then try some of the provided examples. The Matlab interface is also popular but requires a little more work to get started; please see its README for more info.

If you are not sure what you are looking for then try browsing through the code which has submodule links to different repositories for tools and devices (Apps) and language interfaces (LSL). When you land in a new repository then be sure to read its README and look at its Releases page.

Getting Help

If you are having trouble with LSL, there are few things you can do to get help.

• Read the docs
• Search GitHub issues in this repository, in the old archived repository, and in the submodule for your App or language interface of interest.
• Create a new GitHub issue. Please use the repository specific to the item you are having difficulty with. e.g. if you are having trouble with LabRecorder then open a new issue in its repository. If you don't know which repository is best then you can use the parent sccn/labstreaminglayer repository.
• Join the LabStreamingLayer #users channel on Slack. Invite Link. Someone there may be able to get to the bottom of your problem through conversation.
• You may also wish to subscribe to the LSL mailing list

Acknowledgements

The original version of this software was written at the Swartz Center for Computational Neuroscience, UCSD. This work was funded by the Army Research Laboratory under Cooperative Agreement Number W911NF-10-2-0022 as well as through NINDS grant 3R01NS047293-06S1.