Abstract
Keywords: Brain/Behavior Quantification and Synchronization (BBQS), Multimodal recording, Mobile Brain/Body Recording (MoBI), Real-time synchronization Abstract: Accurately recording the interactions of humans or other organisms with their environment and other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) framework offers a software-based approach to syn- chronizing data streams based on per-sample time stamps and time synchronization across a common local area network (LAN). Built from the ground up for neurophysiological applica- tions and designed for reliability, LSL offers zero-configuration functionality and accounts for 2 1 INTRODUCTION network delays and jitters, making connection recovery, offset correction, and jitter compen- sation possible. These features can ensure continuous, millisecond-precise data recording, even in the face of interruptions. In this paper, we present an overview of LSL architec- ture, core features, and performance in common experimental contexts. We also highlight practical considerations and known pitfalls when using LSL, including the need to take into account input device throughput delays that LSL cannot itself measure or correct. The LSL ecosystem has grown to support over 150 data acquisition device classes and to establish interoperability between client software written in several programming languages including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and ver- satility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording, now supported by a wide range of software packages including major stimulus presentation tools, real-time analysis environments, and brain-computer in- terface applications. Beyond basic science, research, and development, LSL has been used as a resilient and transparent back-end in deployment scenarios including interactive art instal- lations, stage performances, and commercial products. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes occurring within and across multiple data streams on a common timeline.
Authors
Christian Kothe, Seyed Yahya Shirazi , Tristan Stenner, David Medine, Chadwick Boulay, Matthew I. Grivich, Fiorenzo Artoni , Tim Mullen, Arnaud Delorme, Scott Makeig
https://doi.org/10.1162/IMAG.a.136