BCI-MCP Server

# 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. [](https://enkhbold470.github.io/bci-mcp/) [](https://opensource.org/licenses/MIT) ## 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 ```bash # 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: ```bash # 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 ```bash # 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/](https://enkhbold470.github.io/bci-mcp/) ### Maintaining the Documentation The documentation is built using MkDocs with the Material theme. To update the documentation: 1. Make changes to the Markdown files in the `docs/` directory on the `main` branch 2. Commit and push your changes to the `main` branch 3. The GitHub Actions workflow will automatically build and deploy the updated documentation to GitHub Pages ### Local Documentation Development To work with the documentation locally: 1. Install the required dependencies: ```bash pip install mkdocs-material mkdocstrings mkdocstrings-python ``` 2. Run the local server: ```bash mkdocs serve ``` 3. 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. 1. Fork the repository 2. Create a feature branch (`git checkout -b feature/amazing-feature`) 3. Commit your changes (`git commit -m 'Add some amazing feature'`) 4. Push to the branch (`git push origin feature/amazing-feature`) 5. Open a Pull Request ## License This project is licensed under the MIT License - see the LICENSE file for details. ## Acknowledgments - Inspired by the [OpenBCI](https://openbci.com/) project - Built on the [Model Context Protocol](https://modelcontextprotocol.io/) framework - Thanks to the neuroscience and AI research communities ## Contact Enkhbold Ganbold - [GitHub Profile](https://github.com/enkhbold470) Project Link: [https://github.com/enkhbold470/bci-mcp](https://github.com/enkhbold470/bci-mcp)