Brain-Computer Interface with Model Context Protocol (BCI-MCP)
This project integrates Brain-Computer Interface (BCI) technology with the Model Context Protocol (MCP) to create a powerful framework for neural signal acquisition, processing, and AI-enabled interactions.
Overview
BCI-MCP combines:
- Brain-Computer Interface (BCI): Real-time acquisition and processing of neural signals
- Model Context Protocol (MCP): Standardized AI communication interface
This integration enables advanced applications in healthcare, accessibility, research, and human-computer interaction.
Key Features
BCI Core Features
- Neural Signal Acquisition: Capture electrical signals from brain activity in real-time
- Signal Processing: Preprocess, extract features, and classify brain signals
- Command Generation: Convert interpreted brain signals into commands
- Feedback Mechanisms: Provide feedback to help users improve control
- Real-time Operation: Process brain activity with minimal delay
MCP Integration Features
- Standardized Context Sharing: Connect BCI data with AI models using MCP
- Tool Exposure: Make BCI functions available to AI applications
- Composable Workflows: Build complex operations combining BCI signals and AI processing
- Secure Data Exchange: Enable privacy-preserving neural data transmission
System Architecture
The BCI-MCP system consists of several key components:
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ BCI Hardware │──────│ BCI Software │──────│ MCP Server │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └────────┬────────┘
│
│
┌────────▼────────┐
│ │
│ AI Applications │
│ │
└─────────────────┘
Getting Started
Prerequisites
- Python 3.10+
- Compatible EEG hardware (or use simulated mode for testing)
- Additional dependencies listed in requirements.txt
Installation
# Clone the repository
git clone https://github.com/enkhbold470/bci-mcp.git
cd bci-mcp
# Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
Using Docker
For easier setup, you can use Docker:
# Build and start all services
docker-compose up -d
# Access the documentation at http://localhost:8000
# The MCP server will be available at ws://localhost:8765
Basic Usage
# Start the MCP server
python src/main.py --server
# Or use the interactive console
python src/main.py --interactive
# List available EEG devices
python src/main.py --list-ports
# Record a 60-second BCI session
python src/main.py --port /dev/tty.usbmodem1101 --record 60
Advanced Applications
The BCI-MCP integration enables a range of cutting-edge applications:
Healthcare and Accessibility
- Assistive Technology: Enable individuals with mobility impairments to control devices
- Rehabilitation: Support neurological rehabilitation with real-time feedback
- Diagnostic Tools: Aid in diagnosing neurological conditions
Research and Development
- Neuroscience Research: Facilitate studies of brain function and cognition
- BCI Training: Accelerate learning and adaptation to BCI control
- Protocol Development: Establish standards for neural data exchange
AI-Enhanced Interfaces
- Adaptive Interfaces: Interfaces that adjust based on neural signals and AI assistance
- Intent Recognition: Better understanding of user intent through neural signals
- Augmentative Communication: Enhanced communication for individuals with speech disabilities
Documentation
The project documentation is hosted on GitHub Pages at: https://enkhbold470.github.io/bci-mcp/
Maintaining the Documentation
The documentation is built using MkDocs with the Material theme. To update the documentation:
- Make changes to the Markdown files in the
docs/directory on themainbranch - Commit and push your changes to the
mainbranch - The GitHub Actions workflow will automatically build and deploy the updated documentation to GitHub Pages
Local Documentation Development
To work with the documentation locally:
-
Install the required dependencies:
pip install mkdocs-material mkdocstrings mkdocstrings-python -
Run the local server:
mkdocs serve -
View the documentation at: http://localhost:8000
Project Structure
.
├── docs/ # Documentation files
│ ├── api/ # API Documentation
│ ├── features/ # Feature Documentation
│ ├── getting-started/ # Getting Started Guides
│ └── index.md # Documentation Home Page
├── mkdocs.yml # MkDocs Configuration
└── .github/workflows/ # GitHub Actions Workflows
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
- Inspired by the OpenBCI project
- Built on the Model Context Protocol framework
- Thanks to the neuroscience and AI research communities
Contact
Enkhbold Ganbold - GitHub Profile
Project Link: https://github.com/enkhbold470/bci-mcp