Lab Streaming Layer

LSL assigns shared timestamps to incoming time-series data so events from separate devices can be placed on a common temporal reference. Because individual instruments may maintain different clocks, these timestamps help relate behavioral responses to measurements collected elsewhere. Buffering further supports alignment when streams arrive with differences in transport timing or sampling behavior.

Stream metadata describes the identity and characteristics of each published data stream, helping receiving applications distinguish behavioral events from eye-tracking, motion, physiological, or neural recordings. This information supports organized multimodal collection and makes it easier to interpret which measurements belong together when several instruments and task applications operate simultaneously.

LSL supports alignment even when connected devices produce data at different sampling rates. Shared timestamps provide the temporal reference, while buffering helps coordinate information arriving from streams with unequal recording rhythms. Researchers can therefore relate discrete task responses to continuous measurements without requiring every instrument or application to collect observations at an identical rate.

A behavioral task application can publish response events as a stream, while laboratory instruments publish their own measurements over the local network. Receiving software then collects these streams and uses their timestamps and metadata to organize them together. This workflow connects task events with concurrent eye tracking, motion, physiological, or neural recordings.

LSL can coordinate behavioral response events with eye-tracking data, motion-capture measurements, physiological signals, and neural recordings. Combining these streams creates a multimodal record in which task performance can be examined alongside bodily, movement-related, visual, or neural measurements. The resulting dataset supports analyses of relationships between observed behavior and accompanying signals.

LSL is particularly useful when an experiment depends on timing relationships across multiple applications or instruments. Its shared timestamps, metadata, and buffering help researchers create synchronized datasets, improving the timing and reproducibility of behavioral experiments. The same coordination also supports more precise investigation of brain-behavior relationships when neural recordings are collected alongside task responses.