Wang Jiyang, Altay Ayse, Hirshfield Leanne, Velipasalar Senem
Electrical Engineering and Computer Science Department, Syracuse University, Syracuse, NY 13244, USA.
Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO 80309, USA.
Sensors (Basel). 2025 Jun 7;25(12):3593. doi: 10.3390/s25123593.
Functional near-infrared spectroscopy (fNIRS) is a non-intrusive way to measure cortical hemodynamic activity. Predicting cognitive workload from fNIRS data has taken on a diffuse set of methods. To be applicable in real-world settings, models are needed, which can perform well across different sessions as well as different subjects. However, most existing works assume that training and testing data come from the same subjects and/or cannot generalize well across never-before-seen subjects. Additional challenges imposed by fNIRS data include not only the high variations in inter-subject fNIRS data but also the variations in intra-subject data collected across different blocks of sessions. To address these challenges, we propose an effective method, referred to as the block-wise domain adaptation (BWise-DA), which explicitly minimizes intra-session variance as well by viewing different blocks from the same subject and same session as different domains. We minimize the intra-class domain discrepancy and maximize the inter-class domain discrepancy accordingly. In addition, we propose an MLPMixer-based model for workload prediction. Experimental results demonstrate that the proposed model provides better performance compared to three different baseline models on three publicly-available workload datasets. Two of the datasets are collected from -back tasks and one of them is from finger-tapping. Moreover, the experimental results show that our proposed contrastive learning method can also be leveraged to improve the performance of the baseline models. We also present a visualization study showing that the models are paying attention to the right regions in the brain, which are known to be involved in the respective tasks.
功能近红外光谱技术（fNIRS）是一种测量皮质血流动力学活动的非侵入性方法。从fNIRS数据预测认知工作量采用了一系列不同的方法。为了适用于现实世界的场景，需要能够在不同会话以及不同受试者之间都表现良好的模型。然而，大多数现有工作假设训练和测试数据来自相同的受试者，并且/或者在从未见过的受试者上不能很好地泛化。fNIRS数据带来的额外挑战不仅包括受试者间fNIRS数据的高度变化，还包括在不同会话块中收集的受试者内数据的变化。为了应对这些挑战，我们提出了一种有效的方法，称为逐块域适应（BWise - DA），通过将来自同一受试者和同一会话的不同块视为不同域，该方法还明确最小化了会话内方差。相应地，我们最小化类内域差异并最大化类间域差异。此外，我们提出了一种基于MLPMixer的工作量预测模型。实验结果表明，与三个公开可用的工作量数据集上的三种不同基线模型相比，所提出的模型具有更好的性能。其中两个数据集是从 -back任务中收集的，另一个是从手指敲击任务中收集的。此外，实验结果表明，我们提出的对比学习方法也可以用于提高基线模型的性能。我们还进行了一项可视化研究，表明模型关注的是大脑中已知与各自任务相关的正确区域。
