用大模型搭建人机协作空间，提升脑机接口研发效率
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research
- 基于双重视角设计原则，构建人机协同工作流
- 通过ChatBCI工具在运动想象解码任务中成功应用
- 适合脑机接口与神经科学研究者快速落地实验
近年来，人工智能正被用于自动化科研流程，甚至实现从想法生成、数据分析到论文撰写与评估的全周期自主科研。已有系统在计算机科学和分子生物学领域展示出可行性。然而，我们主张在脑机接口（BCI）及神经科学领域，应优先采用人机协作模式而非完全自主的AI研究者。本文提出基于双向设计原则的协作工作空间概念，强调兼顾人类与AI的交互需求。据此开发了ChatBCI——一个基于Python、依托大语言模型（LLMs）的开源工具箱，专为BCI研究与开发设计。实证显示，该工具已在一项基于脑电图（EEG）信号的运动想象解码项目中有效应用。本方法可便捷拓展至更广泛的神经技术与神经科学课题，并有助于将人类专家知识系统性传递给科学型AI。
原文摘要 · Abstract (English)
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.
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