使用大语言模型的人机协同：推动脑机接口（BCI）与脑科学研究
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research
AI总结：
本文提出基于Janusian设计原则的人机协同框架及ChatBCI工具箱，通过与大语言模型交互推动脑机接口和脑科学研究，并在运动想象EEG解码项目中验证其有效性。
摘要：
近年来，人们越来越关注利用人工智能（AI）来自动化研究过程的某些环节，甚至自主完成从创意生成、数据分析到科学手稿撰写与评估的完整研究周期。已有的AI科学家系统示例已在计算机科学任务和运行分子生物学实验室中得到展示。虽然一些方法旨在实现科学AI的完全自主，但另一些方法则更倾向于利用人机协同。在此，我们探讨如何调整这类方法以推动脑机接口（BCI）的发展，以及更广泛的脑研究/神经科学。我们认为，在当前阶段，与完全自主的AI BCI研究者相比，强调人机协同将是最有前景的前进方向。我们基于一组Janusian设计原则引入了用于人机协同的协作工作空间概念，同时面向人类和AI两侧。基于这些原则，我们提出了ChatBCI，这是一个基于Python的工具箱，通过与大型语言模型（LLMs）交互来实现人机协作，专为BCI研究和开发项目设计。我们展示了ChatBCI如何在一个具体的BCI项目中成功使用，该项目旨在推进基于EEG信号的运动想象解码。我们的方法可以直接扩展到广泛的神经技术和神经科学主题，并且通过设计可以促进人类专家知识向科学AI系统的普遍迁移。
英文摘要：
Recently, there is an increasing interest in using artificial intelligence (AI) to automate aspects of the research process, or even autonomously conduct the full research cycle from idea generation, over data analysis, to composing and evaluation of scientific manuscripts. Examples of working AI scientist systems have been demonstrated for computer science tasks and running molecular biology labs. While some approaches aim for full autonomy of the scientific AI, others rather aim for leveraging human-AI teaming. Here, we address how to adapt such approaches for boosting Brain-Computer Interface (BCI) development, as well as brain research resp. neuroscience at large. We argue that at this time, a strong emphasis on human-AI teaming, in contrast to fully autonomous AI BCI researcher will be the most promising way forward. We introduce the collaborative workspaces concept for human-AI teaming based on a set of Janusian design principles, looking both ways, to the human as well as to the AI side. Based on these principles, we present ChatBCI, a Python-based toolbox for enabling human-AI collaboration based on interaction with Large Language Models (LLMs), designed for BCI research and development projects. We show how ChatBCI was successfully used in a concrete BCI project on advancing motor imagery decoding from EEG signals. Our approach can be straightforwardly extended to broad neurotechnological and neuroscientific topics, and may by design facilitate human expert knowledge transfer to scientific AI systems in general.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
