Title: An Interpretable Transformer-Based Foundation Model for Cross-Procedural Skill Assessment Using Raw fNIRS Signals Show Chinese title

Title: 基于可解释的Transformer基础模型的跨过程技能评估方法，使用原始fNIRS信号

Abstract: Objective skill assessment in high-stakes procedural environments requires models that not only decode underlying cognitive and motor processes but also generalize across tasks, individuals, and experimental contexts. While prior work has demonstrated the potential of functional near-infrared spectroscopy (fNIRS) for evaluating cognitive-motor performance, existing approaches are often task-specific, rely on extensive preprocessing, and lack robustness to new procedures or conditions. Here, we introduce an interpretable transformer-based foundation model trained on minimally processed fNIRS signals for cross-procedural skill assessment. Pretrained using self-supervised learning on data from laparoscopic surgical tasks and endotracheal intubation (ETI), the model achieves greater than 88% classification accuracy on all tasks, with Matthews Correlation Coefficient exceeding 0.91 on ETI. It generalizes to a novel emergency airway procedure--cricothyrotomy--using fewer than 30 labeled samples and a lightweight (less than 2k parameter) adapter module, attaining an AUC greater than 87%. Interpretability is achieved via a novel channel attention mechanism--developed specifically for fNIRS--that identifies functionally coherent prefrontal sub-networks validated through ablation studies. Temporal attention patterns align with task-critical phases and capture stress-induced changes in neural variability, offering insight into dynamic cognitive states. Show Chinese abstract

Abstract: 客观技能评估在高风险程序环境中需要不仅能够解码潜在的认知和运动过程，而且能够在任务、个体和实验情境之间泛化的模型。 尽管之前的工作已经证明了功能性近红外光谱（fNIRS）在评估认知-运动表现方面的潜力，但现有方法通常是任务特定的，依赖于大量的预处理，并且对新程序或条件缺乏鲁棒性。 在此，我们引入了一个可解释的基于变压器的基础模型，该模型使用最少处理的fNIRS信号进行跨程序技能评估。 通过在腹腔镜手术任务和气管插管（ETI）数据上使用自监督学习进行预训练，该模型在所有任务上的分类准确率超过88%，在ETI上的马修斯相关系数超过0.91。 它使用少于30个标记样本和一个轻量级（少于2k参数）的适配器模块，推广到一种新的紧急气道程序——环甲膜切开术，达到AUC大于87%。 通过一种新颖的通道注意力机制实现可解释性——专门为fNIRS开发的机制——该机制识别经过消融研究验证的功能一致的前额叶子网络。 时间注意力模式与任务关键阶段对齐，并捕捉神经变异性中的应激诱导变化，提供对动态认知状态的见解。