Asynchronous High-Frequency SSVEP Brain-Computer Interface in Virtual Reality

This repository contains a collection of tools for analyzing and classifying Steady-State Visual Evoked Potential (SSVEP) Brain-Computer Interfaces (BCI) with high-frequency stimuli. It includes code for classification using Filter Bank Canonical Correlation Analysis (FBCCA) with filters specifically designed for frequencies in the 30 Hz and higher range.

Key features:

• SSVEP analysis tools for high-frequency stimuli
• FBCCA classification implementation
• Custom filter designs for 30+ Hz frequencies
• EEG data processing utilities

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Installation

Prerequisites

Ensure the following are installed:

• Python 3.8 or later
• pip (Python package manager)
• Required hardware:
  • EEG System: BioSemi ActiveTwo or compatible device

Steps

1. Clone the repository:

   git clone https://github.com/jiapulidoar/HF-SSVEP
   cd ssvep-bci
   

2. Set up a virtual environment (optional but recommended):

   python -m venv env
   source env/bin/activate  # On Windows: env\Scripts\activate

3. Install the required Python packages:

   pip install -r requirements.txt
   

Usage

1. Setup BioSemi ActiveTwo and start TCP server in port 8888 and sampling rate 512Hz.

2. Online Experiment: Configure the system settings in eegtools\config.py and run:

   python eegtools\onlineReceiveData.pyeegtools\config.py