Frequently Asked Questions (FAQ) about BCI-MCP

Q: What is BCI-MCP?

A: BCI-MCP stands for Brain-Computer Interface with Model Context Protocol. It’s a project that integrates BCI technology with the MCP to enable advanced neural signal processing and AI-enabled interactions.

Q: What are the key features of BCI-MCP?

A: Key features include real-time neural signal acquisition and processing, standardized context sharing with AI models via MCP, composable workflows, and secure data exchange.

Q: What are some potential applications of BCI-MCP?

A: Potential applications span healthcare (assistive tech, rehab), research (neuroscience, BCI training), and AI-enhanced interfaces (adaptive interfaces, intent recognition).

Q: What is the Model Context Protocol (MCP)?

A: MCP is an open protocol that standardizes how applications provide context to LLMs. In the context of BCI-MCP, it allows AI models to access and interact with neural data.

Q: What prerequisites are needed to use BCI-MCP?

A: You need Python 3.10+, compatible EEG hardware (or simulated mode), and dependencies listed in requirements.txt.

Q: How do I install BCI-MCP?

A: You can install it by cloning the repository, creating a virtual environment, and installing dependencies using pip. Alternatively, you can use Docker for an easier setup.

Q: How can I contribute to the BCI-MCP project?

A: Contributions are welcome! You can fork the repository, create a feature branch, commit your changes, and submit a pull request.

Q: Is BCI-MCP open-source?

A: Yes, the project is licensed under the MIT License, making it freely available for research, development, and commercial use.

Q: Where can I find the documentation for BCI-MCP?

A: The project documentation is hosted on GitHub Pages.

Q: Can I use BCI-MCP with UBOS?

A: Yes, BCI-MCP can be integrated with UBOS. UBOS is a full-stack AI Agent Development Platform. UBOS focused on bringing AI Agent to every business department. Our platform help you orchestrate AI Agents, connect them with your enterprise data, build custom AI Agents with your LLM model and Multi-Agent Systems.