Kothe Christian, Shirazi Seyed Yahya, Stenner Tristan, Medine David, Boulay Chadwick, Grivich Matthew I, Artoni Fiorenzo, Mullen Tim, Delorme Arnaud, Makeig Scott
Intheon Labs, San Diego, CA, United States.
Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA, United States.
Imaging Neurosci (Camb). 2025 Sep 12;3. doi: 10.1162/IMAG.a.136. eCollection 2025.
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 synchronizing 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 applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation 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 architecture, 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 versatility 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 interface applications. Beyond basic science, research, and development, LSL has been used as a resilient and transparent back-end in deployment scenarios, including interactive art installations, 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.
准确记录人类或其他生物体与环境及其他因素的相互作用，需要通过多种仪器同步数据访问，这些仪器通常使用不同的时钟独立运行。对于任意输入系统集合而言，主动式的硬件介导解决方案往往不可行，或者构建和运行成本过高。实验室流层（LSL）框架提供了一种基于软件的方法，可根据每个样本的时间戳以及跨通用局域网（LAN）的时间同步来同步数据流。LSL专为神经生理学应用而构建，设计可靠，具有零配置功能，并能处理网络延迟和抖动，从而实现连接恢复、偏移校正和抖动补偿。即使面对中断情况，这些特性也能确保连续的、精确到毫秒的数据记录。在本文中，我们概述了LSL的架构、核心特性以及在常见实验环境中的性能。我们还强调了使用LSL时的实际注意事项和已知陷阱，包括需要考虑LSL自身无法测量或校正的输入设备吞吐量延迟。LSL生态系统已发展到支持超过150种数据采集设备类别，并在多种编程语言（包括C/C++、Python、MATLAB、Java、C#、JavaScript、Rust和Julia）编写的客户端软件之间建立了互操作性。LSL的弹性和通用性使其成为多模态人类神经行为记录的主要数据同步平台，目前得到了广泛的软件包支持，包括主要的刺激呈现工具、实时分析环境和脑机接口应用。除了基础科学研究与开发之外，LSL还在部署场景中用作弹性且透明的后端，包括交互式艺术装置、舞台表演和商业产品。在神经行为研究和其他神经科学应用中，LSL有助于在共同时间轴上捕获多个数据流内部和之间发生的生物体动态和环境变化这一复杂任务。
