P300-Based Brain–Computer Interface Integrated with AI Agents for Intelligent Human–Machine Interaction
DOI:
https://doi.org/10.71146/kjmr967Keywords:
Brain–Computer Interface, P300, EEG, Event-Related Potential, Artificial Intelligence, Deep Learning, Large Language Model, Neural Engineering, Human–Machine Interaction, PrivacyAbstract
This paper presents a comprehensive review of P300-based Brain–Computer Interfaces (BCIs) integrated with intelligent AI agents. We explain EEG-based BCIs and the P300 oddball paradigm, which yields a reliable positive deflection approximately 300 ms after a rare stimulus. We survey classic P300-speller systems and signal-processing pipelines, and how machine learning and deep learning—particularly EEGNet—enhance P300 detection. We then focus on AI-agent integration: recent systems (ChatBCI, MindChat) combine P300 spellers with large language models (LLMs) to cut keystrokes by over 62% and nearly triple communication speed. We analyze neuro-symbolic AI and modular cognitive architectures that enable context-aware, autonomous BCI agents. A proposed end-to-end architecture integrates an EEG headset with a real-time CNN-based P300 detector and an AI-agent layer combining an LLM-based planner with IoT control APIs. We discuss hardware and software components, latency, privacy-preserving measures, and experimental evaluation strategy. We conclude by examining applications in assistive communication, smart home control, and AR/VR, alongside challenges of signal noise, user fatigue, and neural data ethics.
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Copyright (c) 2026 Muhammad Talha (Author)
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