TittaLSL: A toolbox for creating networked eye-tracking experiments in Python and MATLAB with Tobii eye trackers

Area of Science:

• Cognitive Science
• Neuroscience
• Human-Computer Interaction

Background:

• Networked eye-tracking studies are gaining traction.
• Existing solutions for streaming gaze data are complex and require significant programming.
• A need exists for simplified tools to facilitate multi-participant networked eye-tracking.

Purpose of the Study:

• To introduce TittaLSL, a toolbox for networked multi-participant eye-tracking experiments.
• To enable researchers to use Tobii eye trackers in networked setups with minimal programming.
• To evaluate the performance and latency of the TittaLSL toolbox.

Main Methods:

• Developed TittaLSL toolbox for streaming gaze data over a local network.
• Utilized Tobii eye trackers for data acquisition.
• Evaluated latency using 600-Hz gaze streams across 15 networked eye-tracking stations.

Main Results:

• TittaLSL enables networked multi-participant experiments with minimal programming.
• Achieved an end-to-end latency of 3.05 ms for streamed gaze data.
• Latency was only 0.10 ms higher than local connections, suitable for real-time applications.

Conclusions:

• TittaLSL significantly simplifies the creation of networked multi-participant eye-tracking studies.
• The low latency of TittaLSL supports real-time gaze visualization and analysis.
• This toolbox is a valuable resource for researchers in various fields utilizing eye-tracking technology.