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 the main branch
Commit and push your changes to the main branch
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

BCI-MCP Server

Integrations