BCI-MCP Server

# Brain-Computer Interface with Model Context Protocol Welcome to the documentation for BCI-MCP, an integration of Brain-Computer Interface (BCI) technology with the Model Context Protocol (MCP) for advanced neural signal acquisition, processing, and AI-enabled interactions. ## Overview BCI-MCP combines the power of: - **Brain-Computer Interface (BCI)**: Real-time acquisition and processing of neural signals - **Model Context Protocol (MCP)**: Standardized AI communication interface This integration enables a wide range of 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 ## 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 ## Getting Started To start using BCI-MCP, check out our [Quick Start Guide](getting-started/quick-start.md). ## Architecture The BCI-MCP system consists of several key components: ``` ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ │ │ │ │ │ │ BCI Hardware │──────│ BCI Software │──────│ MCP Server │ │ │ │ │ │ │ └─────────────────┘ └─────────────────┘ └────────┬────────┘ │ │ ┌────────▼────────┐ │ │ │ AI Applications │ │ │ └─────────────────┘ ``` ## Documentation Status This documentation is automatically built and deployed using GitHub Actions when changes are made to the main branch. ## Contributing We welcome contributions from the community! Check out our [Contributing Guide](contributing.md) to learn how you can help. ## License This project is licensed under the MIT License - see the LICENSE file in the repository for details. ## New Update (2025-03-23) This is a test update to trigger the documentation workflow. The documentation should be automatically deployed to GitHub Pages.