PhysioLabXR

A Python Platform for Real-Time, Multi-modal, Brain–Computer Interfaces and Extended Reality Experiments
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

PhysioLabXR is a Python-based App for real-time visualizing, recording, and processing (i.e., training ML models and inferencing) data streams. PhysioLabXR can help you build novel interaction interfaces like BCIs and aid you in running experiments. It works best with multi-modal (e.g., combining EEG and eyetracking, camera with speech), high-throughput (>500Mbps/sec), and real-time data streams.

Check out the paper on Journal of Open Source Software: . Cite:

@article{Li2024, doi = {10.21105/joss.05854}, url = {https://doi.org/10.21105/joss.05854}, year = {2024}, publisher = {The Open Journal}, volume = {9}, number = {93}, pages = {5854}, author = {Ziheng 'Leo' Li and Haowen 'John' Wei and Ziwen Xie and Yunxiang Peng and June Pyo Suh and Steven Feiner and Paul Sajda}, title = {PhysioLabXR: A Python Platform for Real-Time, Multi-modal, Brain–Computer Interfaces and Extended Reality Experiments}, journal = {Journal of Open Source Software} }

Built With

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Getting Started

Download and run the executable

Download the latest release exe from here, available for Windows, Mac, and Linux.

Install with pip

PhysioLabXR currently supports Python 3.9, 3.10, and 3.11. Support for Python 3.12 is coming soon.

Install PhysioLabXR's PYPI distribution with

  pip install physiolabxr

Then run with

  physiolabxr

Run from Source

Alternatively, you can clone the repo and run it from the source.

  git clone https://github.com/PhysioLabXR/PhysioLabXR.git
  cd PhysioLabXR
  pip install -r requirements.txt

The entry point to PhysioLabXR is physiolabxr.py, located in the folder named "physiolabxr". From the root folder, you can run it by:

  python physiolabxr/physiolabxr.py

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Usage

For more examples, please refer to the tutorials in the documentation

Tutorials have examples for:

• Real-time fixation detection (link)
• Real-time multi-modal event-related-potential classification with EEG and pupillometry
• P300 speller in Unity
• Stroop task with PsychoPy (link [https://physiolabxrdocs.readthedocs.io/en/latest/PsychoPy.html])

More are coming soon!

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License

Distributed under the 3-Clause BSD License. See LICENSE.txt for more information.

© 2023 The Trustees of Columbia University in the City of New York. This work may be reproduced, distributed, and otherwise exploited for academic non-commercial purposes only. To obtain a license to this work for commercial purposes, please contact Columbia Technology Ventures at techventures@columbia.edu.

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Contact

Ziheng 'Leo' Li - zl2990@columbia.edu

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Acknowledgments

We would like to express our gratitude for the support from our colleagues at Columbia University and Worcester Polytechnic Institute. We would also like to thank all the community members who have contributed to PhysioLabXR.

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