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

TL;DR

This paper advocates for a paradigm shift from traditional Brain-Computer Interfaces to Brain-Agent Collaboration, emphasizing ethical, trustworthy, and effective integration of AI agents as active partners in assistive technologies.

Contribution

It introduces the concept of Brain-Agent Collaboration as an extension of BCI, highlighting the need for ethical, reliable, and collaborative AI systems in assistive tech.

Findings

Highlights limitations of current BCI systems

Proposes a new paradigm emphasizing trust and collaboration

Discusses technical and ethical challenges in AI integration

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.