Team LeBrain - Neureality 2024

Nikhil Kumar Kuppa
Srikanth Chandar
Seokhee Kang
Aymeric Degroote

Requirements

Hardware:

• Unicorn Hybrid Black
• OpenBCI Cyton Board
• Meta Quest 2
• Windows OS

Software:

• Unity Unicorn
• OpenBCI GUI
• PhysioLabXR
• Unity
• Virtual Studio
• Google Colab
• MATLAB
• Meta Quest Mobile App

Technical pipeline

EMG signal

• Option 1: Binary signal Use OpenBCI GUI to acquire the electromyography (EMG) signal, normalize it into values between 0 and 1, and broadcast the data into a Laboratory Stream Layer (LSL). Use PhysioLabXR and the Python script binarize_emg.py to have an LSL with only one channel taking binary values (binary classification).

• Option 1: Raw signal From the library PhysioLabXR, run PhysioLabXR_P300Speller_Demo/PhysioLabXRP300SpellerDemoScript.py to acquire the raw electromyography (EMG) signal straight into a Laboratory Stream Layer (LSL). Use PhysioLabXR and the Python script emg_decoder.py to have an LSL with only one channel with the decoded classes.

EEG signal

Use Unicorn to broadcast the EEG signal into an LSL. Use PhysioLabXR and the Python script eeg_decoder.py to produce an LSL with only one channel with the decoded classes.

SSVEP signal

Use Unicorn to broadcast the EEG signal into an LSL. Use PhysioLabXR and the Python script ssvep_decoder.py (not in the repository) to produce an LSL with the identified frequency.

Training decoders

We used MATLAB to acquire the training data and stored it in csv files. We implemented the models in Tensorflow Keras and used Jupyter notebooks for the training process.

Unity

Add ActionLSLInput.cs to the game scripts. It leverages the LSL4Unity library to read LSL streams and stores the relevant information in the class attributes. The script PlayerControl.cs reads the attributes of ActionLSLInput to switch the weapon. For now, we implemented the weapon switching through the binary EMG signal. Eventually, all modalities would have been integrated.