用真实课堂数据微调脑电大模型，识别压力状态准确率达90.47%。
From Theory to Application: Fine-Tuning Large EEG Model with Real-World Stress Data
- 在真实课堂环境数据上微调LaBraM模型，实现高效压力分类
- 5秒窗口下平衡准确率达90.47%，显著优于传统方法
- 适合关注脑机接口实际应用的研究者与开发者
近年来大型语言模型的发展推动了各领域的基础模型进步。本研究通过在真实课堂环境中采集的应激分类数据集上微调当前最先进的脑电基础模型LaBraM，评估其有效性。不同于以往多基于受控临床数据的评估方式，本工作聚焦真实世界场景下的适用性。我们利用18名研究生在课程期间记录的静息态脑电信号，训练一个二分类器以区分正常与高应激状态。最优微调模型在5秒时间窗内达到90.47%的平衡准确率，显著提升准确率与推理效率。进一步测试表明，该模型在随机数据打乱及通道数减少情况下仍具鲁棒性。结果证明，脑电大模型能有效处理真实世界脑电数据，有望推动脑机接口从模型主导转向数据驱动的设计范式。
原文摘要 · Abstract (English)
Recent advancements in Large Language Models have inspired the development of foundation models across various domains. In this study, we evaluate the efficacy of Large EEG Models (LEMs) by fine-tuning LaBraM, a state-of-the-art foundation EEG model, on a real-world stress classification dataset collected in a graduate classroom. Unlike previous studies that primarily evaluate LEMs using data from controlled clinical settings, our work assesses their applicability to real-world environments. We train a binary classifier that distinguishes between normal and elevated stress states using resting-state EEG data recorded from 18 graduate students during a class session. The best-performing fine-tuned model achieves a balanced accuracy of 90.47% with a 5-second window, significantly outperforming traditional stress classifiers in both accuracy and inference efficiency. We further evaluate the robustness of the fine-tuned LEM under random data shuffling and reduced channel counts. These results demonstrate the capability of LEMs to effectively process real-world EEG data and highlight their potential to revolutionize brain-computer interface applications by shifting the focus from model-centric to data-centric design.
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