Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research

2024-12-30

AI Summary

To address inefficiencies in human-AI collaboration, poor model interpretability, and difficulties in knowledge transfer within brain-computer interface (BCI) research, this paper proposes a bidirectional human-AI co-creation paradigm guided by the Janusian dual-perspective principle—explicitly delineating complementary human and AI roles—and establishes an interpretable, controllable collaborative workspace. We introduce ChatBCI, the first open-source Python toolbox integrating large language models (LLMs), EEG signal processing, and interactive reasoning frameworks to enable dynamic, end-to-end collaboration between domain experts and AI across experimental design, feature engineering, and model interpretation. Evaluated on motor imagery EEG decoding, our approach improves experimental design efficiency by 40% on average and significantly enhances model interpretability. Furthermore, its modular architecture ensures generalizability to broader neurotechnology applications.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Human-Robot Interaction

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

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.

Problem

• Brain-Computer Interface
• AI Optimization
• Motor Imagery Recognition

Innovation

• ChatBCI
• Brain-Computer Interface
• Large Language Model Collaboration