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:

Getting Started

Prerequisites

• Python 3.10+

• Compatible EEG hardware (or use simulated mode for testing)

• Additional dependencies listed in requirements.txt

Installation

Using Docker

For easier setup, you can use Docker:

Basic Usage

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:

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:

   pip install mkdocs-material mkdocstrings mkdocstrings-python

2. Run the local server:

   mkdocs serve

3. View the documentation at: http://localhost:8000

Project Structure

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 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