做可穿戴设备或脑机接口的团队，终于有了一个把临床大模型直接用到低导联设备上的实用方案——CogAdapt的LeadBridge适配器解决了传感器不匹配的痛点，建议做认知负荷评估的开发者直接参考。
实时认知负荷评估对自适应人机交互至关重要，但受限于标注数据少和跨个体泛化差。CogAdapt提出LeadBridge适配器，将3导联可穿戴信号转换为12导联临床格式，并结合ProFine渐进微调策略，防止灾难性遗忘。在CLARE和CL-Drive数据集上，CogAdapt的宏F1分数分别达0.626和0.768，显著优于从头训练的基线模型。该工作证明了基础模型迁移在可穿戴设备上实现个体无关认知负荷评估的可行性。
CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation
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.