Accurately monitoring cognitive load in real time is critical for Brain-Computer Interfaces (BCIs) that adapt to user engagement and support personalized learning. Electroencephalography (EEG) offers a non-invasive, cost-effective modality for capturing neural activity, though traditional methods often struggle with cross-subject variability and task-specific preprocessing. We propose leveraging Brain Foundation Models (BFMs), large pre-trained neural networks, to extract generalizable EEG features for cognitive load estimation. We adapt BFMs for long-term EEG monitoring and show that fine-tuning a small subset of layers yields improved accuracy over the state-of-the-art. Despite their scale, BFMs allow for real-time inference with a longer context window. To address often-overlooked interpretability challenges, we apply Partition SHAP (SHapley Additive exPlanations) to quantify feature importance. Our findings reveal consistent emphasis on prefrontal regions linked to cognitive control, while longitudinal trends suggest learning progression. These results position BFMs as efficient and interpretable tools for continuous cognitive load monitoring in real-world BCIs.

翻译：实时准确监测认知负荷对于适应用户参与度并支持个性化学习的脑机接口至关重要。脑电图作为一种非侵入性、经济高效的神经活动捕获方式，尽管传统方法常受跨被试变异性和任务特定预处理的限制。我们提出利用脑基础模型——大规模预训练神经网络——来提取可泛化的脑电图特征以估计认知负荷。我们将BFMs适配于长期脑电图监测，并证明仅微调少量网络层即可获得超越现有最优方法的精度。尽管模型规模庞大，BFMs仍能通过扩展上下文窗口实现实时推理。针对常被忽视的可解释性挑战，我们应用分区SHAP（沙普利加性解释）量化特征重要性。研究发现模型持续关注与认知控制相关的前额叶区域，而纵向趋势则暗示学习进程。这些成果确立了BFMs作为现实场景脑机接口中持续认知负荷监测的高效可解释工具。