实验室流媒体层用于同步的多式联络记录
Christian Kothe1, Seyed Yahya Shirazi2, Tristan Stenner3
1Intheon Labs, San Diego, CA, United States.
Imaging neuroscience (Cambridge, Mass.)
|September 17, 2025
概括
实验室流媒体层 (LSL) 使用软件同步来自多个仪器的数据,确保神经行为研究的毫秒精确记录. 对于各种应用的复杂数据采集,LSL提供了一种可靠,具有成本效益的解决方案.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
背景情况:
- 从多个仪器同步获取数据对于准确记录生物与环境的相互作用至关重要.
- 基于硬件的同步解决方案通常是昂贵的,并且对于各种系统来说不切实际.
- 现有的方法与独立的仪表时钟,网络延迟和潜在的中断作斗争.
研究的目的:
- 将实验室流媒体层 (LSL) 作为一个基于软件的框架来同步数据流.
- 详细介绍LSL的架构,核心特征以及神经生理学和神经行为应用中的性能.
- 突出使用LSL用于可靠数据记录的实际考虑和局限性.
主要方法:
- 对于数据流对齐,LSL采用每个样本的时间和网络时间同步.
- 该框架包含用于连接恢复,偏移校正和动补偿的功能.
- 基于软件的同步通过局域网 (LAN) 实现.
主要成果:
- 通过LSL,即使有网络中断,也可以连续,精确地记录数据.
- 该框架支持超过150个数据采集设备类和跨多种编程语言的互操作性.
- LSL已被证明具有弹性和多功能性,支持从基础研究到商业产品的各种应用.
结论:
- LSL提供了一个强大的零配置软件解决方案,用于同步多式联网数据流.
- 它促进了捕捉生物的动态和环境变化的复杂任务在一个共同的时间表上.
- LSL是多式联通人类神经行为记录和实时分析的主要平台.
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