用脑电微状态特征微调大模型，提升心理负荷评估准确率
Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
- 将脑电微状态特征融入提示词，指导大模型识别认知状态
- 在指定数据集上实现对'静息'与'负荷'状态的高精度区分
- 适合认知神经科学与脑机接口方向的研究者参考
本研究探索脑电图（EEG）微状态与大语言模型（LLMs）的结合，以提升对认知负荷状态的评估能力。通过利用EEG微状态特征，研究旨在微调大模型，从而更准确地预测两种典型认知状态：'静息'与'负荷'。实验分为四个阶段：数据集采集与预处理、微状态分割与脑电信号回溯、特征提取与提示工程，以及大模型的筛选与优化。采用监督学习范式，大模型基于嵌入提示中的EEG微状态特征进行训练，实现了对认知负荷状态的精准判别。一个经过精心构建的数据集，将EEG特征与特定认知负荷条件关联起来，支撑了整个实验框架。结果表明，经该方法微调后，模型性能显著提升，展示了脑电信息驱动的大模型在认知神经科学与认知人工智能应用中的潜力。该方法不仅有助于理解脑动态机制，也为认知负荷与认知人工智能研究中的机器学习技术发展提供了新路径。
原文摘要 · Abstract (English)
This study explores the intersection of electroencephalography (EEG) microstates and Large Language Models (LLMs) to enhance the assessment of cognitive load states. By utilizing EEG microstate features, the research aims to fine-tune LLMs for improved predictions of distinct cognitive states, specifically 'Rest' and 'Load'. The experimental design is delineated in four comprehensive stages: dataset collection and preprocessing, microstate segmentation and EEG backfitting, feature extraction paired with prompt engineering, and meticulous LLM model selection and refinement. Employing a supervised learning paradigm, the LLM is trained to identify cognitive load states based on EEG microstate features integrated into prompts, producing accurate discrimination of cognitive load. A curated dataset, linking EEG features to specified cognitive load conditions, underpins the experimental framework. The results indicate a significant improvement in model performance following the proposed fine-tuning, showcasing the potential of EEG-informed LLMs in cognitive neuroscience and cognitive AI applications. This approach not only contributes to the understanding of brain dynamics but also paves the way for advancements in machine learning techniques applicable to cognitive load and cognitive AI research.
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