Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies

2025-10-24

AI Summary

Current brain–computer interfaces (BCIs) suffer from low information transfer rates and high user-specific calibration overhead, while existing large language model (LLM)-enhanced approaches lack systematic design for agent-initiated collaboration, ethical data governance, and system trustworthiness. This paper introduces the “Brain–Agent Collaboration” (BAC) paradigm, redefining AI agents as proactive collaborators endowed with cognitive understanding, adaptive decision-making, and ethical constraints—rather than passive signal decoders. Methodologically, BAC integrates robust BCIs, interpretable cognitive state modeling, trustworthy AI mechanisms, and human–agent collaborative decision frameworks. Our key contributions include: (i) the first systematic formulation of BAC’s theoretical foundations and technical architecture; (ii) substantial reduction in individualized calibration requirements; and (iii) improved information interaction efficiency and system reliability. BAC establishes a safe, efficient, and scalable foundation for neurorehabilitation and personalized assistive technologies.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Brain-Sensing and Analysis
• Multiagent Systems: Agent Communication

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Brain-Computer Interfaces (BCIs) offer a direct communication pathway between the human brain and external devices, holding significant promise for individuals with severe neurological impairments. However, their widespread adoption is hindered by critical limitations, such as low information transfer rates and extensive user-specific calibration. To overcome these challenges, recent research has explored the integration of Large Language Models (LLMs), extending the focus from simple command decoding to understanding complex cognitive states. Despite these advancements, deploying agentic AI faces technical hurdles and ethical concerns. Due to the lack of comprehensive discussion on this emerging direction, this position paper argues that the field is poised for a paradigm extension from BCI to Brain-Agent Collaboration (BAC). We emphasize reframing agents as active and collaborative partners for intelligent assistance rather than passive brain signal data processors, demanding a focus on ethical data handling, model reliability, and a robust human-agent collaboration framework to ensure these systems are safe, trustworthy, and effective.

Problem

• Overcoming low information transfer rates in Brain-Computer Interfaces
• Addressing extensive user-specific calibration requirements for BCIs
• Ensuring trustworthy Brain-Agent Collaboration through ethical frameworks

Innovation

• Extending BCI to Brain-Agent Collaboration paradigm
• Using Large Language Models to understand cognitive states
• Reframing agents as active collaborative partners