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

Yankai Chen, Xinni Zhang, Yifei Zhang, Yangning Li, Henry Peng Zou, Chunyu Miao, Weizhi Zhang, Xue Liu, Philip S. Yu

Keywords: Brain Activity Analysis; Brain-Agent Collaboration; LLM, Brain-Computer Interfaces

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.

Lay Summary:

Brain-Computer Interfaces (BCIs) let people control devices with their brain signals, a promising technology for individuals with severe neurological impairments. To further improve this technology, researchers are integrating advanced artificial intelligence (AI). This enhancement allows the systems to move beyond decoding simple commands to understanding more complex cognitive states.

This paper argues that the AI should be more than a passive tool. It proposes a framework called "Brain-Agent Collaboration" (BAC), where the AI acts as an active, collaborative partner. The authors stress that for this human-AI partnership to work, it must be built on a foundation of ethical data handling, model reliability, and robust safety frameworks to ensure it is trustworthy and effective.

Submission Number: 749