Integrating Lab Streaming Layer (LSL) with SightLab VR Pro

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Lab Streaming Layer (LSL) is an open-source middleware ecosystem designed to stream, receive, synchronize, and record neural, physiological, and behavioral data across diverse sensor hardware. By bridging LSL with SightLab VR, researchers can seamlessly incorporate third-party EEG, eye-tracking, and physiological data into their virtual reality experiments, as well as export VR-derived metrics to external acquisition software.

For more information see this page

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Overview of LSL

LSL provides a unified interface for real-time data transmission:

• Networked Streams: Transmit data packets over IP, allowing multiple applications on the same or different machines to share time-synchronized streams.
  
  
• Cross-Platform: Supports Windows, macOS, Linux, iOS, and Android.
  
  
• Broad Device Support: Hundreds of biosignal, motion, and other research-grade sensors are LSL-compatible.
  
  

For a complete list of supported devices and applications, visit the Lab Streaming Layer device list.

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Installing LSL for SightLab

Before integrating LSL with your SightLab project, install the Python bindings:

# In the Vizard IDE: Tools → Package Manager → Add Package

pip install pylsl

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Then, in your script’s imports:

from pylsl import StreamInlet, StreamOutlet, resolve_stream

import sightlab_utils.sightlab as sl

from sightlab_utils.settings import *

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Streaming Into SightLab (Stream Inlet)

To ingest external biosignals (e.g., EEG from Muse or BrainVision) directly into your VR session:

Discover the Stream

# Wait up to 5 seconds for an EEG stream

streams = resolve_stream('type', 'EEG')

inlet  = StreamInlet(streams[0])

1. Add Stream to SightLab Recorder
   
   sightlab = sl.SightLab()

sightlab.addStreamInlet('EEG', inlet)

2. Save to Data Files
   SightLab will automatically timestamp and include incoming samples in its recording CSVs and experiment summary.
   
   

See ExampleScripts/LSL_Sample/lsl_sample_receive.py for a complete implementation, including averaging and experiment-summary integration.

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Streaming Out of SightLab (Stream Outlet)

Export VR-derived metrics—such as gaze coordinates, head orientation, or pupil diameter—to external analysis tools:

Create an LSL Outlet

from pylsl import StreamInfo, StreamOutlet

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info   = StreamInfo('SightLabGaze', 'Gaze', 3, 0.0, 'float32', 'sightlab123')

outlet = StreamOutlet(info)

sightlab.addStreamOutlet('Gaze', outlet)

1. Push Data During Trials
   Inside your trial loop or callback:
   
   gaze_pos = sightlab.getCurrentGazePosition()  # [x, y, z]

outlet.push_sample(gaze_pos)

2. Use in External Software
   Common applications include:
   
   
   
   • BioPac AcqKnowledge
     
     
   • MATLAB (via LSL MATLAB API)
     
     
   • BrainVision Recorder
     
     

See ExampleScripts/LSL_Sample/lsl_sample_stream.py for generic outlet setup.

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Workflow: SightLab → Biopac AcqKnowledge

AcqKnowledge now natively supports LSL enabling VR-derived streams to be captured alongside physiological channels.

1. Enable LSL in AcqKnowledge
   
   
   
   • Display → Preferences → Hardware → Data Acquisition
     
     
   • Check “LSL” as a hardware source.
     
     
2. Add LSL Stream
   
   
   
   • In your graph window, click “+” → LSL.
     
     
   • Restart AcqKnowledge to refresh hardware options.
     
     
3. Connect to SightLab Outlet
   
   
   
   • In a new experiment (File → New), select LSL from the hardware dropdown.
     
     
   • Press “Refresh” when your SightLab script is running.
     
     
4. Visualize & Record
   
   
   
   • VR metrics (e.g., gaze path, pupil size) appear as channels in AcqKnowledge.

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Troubleshooting Tips

• “No Stream Available” upon launch?
  
  
  • Start your SightLab script to open the LSL outlet.
    
    
  • Hit “Refresh” in AcqKnowledge’s hardware menu.
    
    
• Outlet Unexpectedly Closes
  
  
  • If SightLab exits or the outlet is closed, go to “Manage Hardware Connections” in AcqKnowledge to rediscover streams.
    
    
• Latency Concerns
  
  
  • Ensure both applications are on the same LAN and that firewall settings permit UDP traffic on the LSL port range (default: 11000–11999).
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Conclusion

By leveraging LSL within SightLab, researchers gain a versatile pipeline for incorporating and exporting rich, synchronized biosignal and behavioral data. Whether streaming in EEG for closed-loop VR paradigms or pushing gaze and kinematic metrics to Biopac AcqKnowledge, the integration empowers complex, multimodal experiments with precise timing and minimal overhead.