Real-time cognitive load assessment is essential for adaptive human-computer interaction but remains challenging due to limited labeled data and poor cross-subject generalization. Recent ECG foundation models pre-trained on millions of clinical recordings offer rich representations, but cannot be directly applied to wearable devices due to sensor configuration mismatch and task differences. In this paper, we propose CogAdapt, a framework that adapts clinical ECG foundation models to wearable cognitive load assessment. CogAdapt introduces LeadBridge, a learnable adapter that transforms 3-lead wearable signals into anatomically consistent 12-lead representations, and ProFine, a progressive fine-tuning strategy that gradually unfreezes encoder layers while preventing catastrophic forgetting. Evaluations on two public datasets (CLARE and CL-Drive) under leave-one-subject-out cross-validation show that CogAdapt substantially outperforms baselines trained from scratch, achieving macro-F1 scores of 0.626 and 0.768. These results demonstrate the promise of foundation model adaptation for subject-independent cognitive load assessment from wearable sensors.

翻译：实时认知负荷评估对于自适应人机交互至关重要，但由于标注数据有限且跨受试者泛化能力差，仍面临挑战。近期基于数百万临床记录预训练的心电图基础模型提供了丰富的表征，但由于传感器配置不匹配和任务差异，无法直接应用于可穿戴设备。本文提出CogAdapt框架，用于将临床心电图基础模型适应于可穿戴认知负荷评估。CogAdapt引入可学习适配器LeadBridge，将3导联可穿戴信号转换为解剖结构一致的12导联表征，并采用渐进式微调策略ProFine，该策略逐步解冻编码器层同时防止灾难性遗忘。在CLARE和CL-Drive两个公开数据集上进行的留一受试者交叉验证评估表明，CogAdapt显著优于从头训练的基线模型，宏平均F1分数分别达到0.626和0.768。这些结果证明了基础模型自适应方法在基于可穿戴传感器的受试者无关认知负荷评估中的潜力。