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 the high variations in inter-subject fNIRS data and also in intra-subject data collected across different blocks of sessions. To address these issues, we propose an effective method, referred to as the class-aware-block-aware domain adaptation (CABA-DA) which explicitly minimize intra-session variance by viewing different blocks from the same subject 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 cognitive load classification. Experimental results demonstrate the proposed model has better performance compared with three different baseline models on three public-available datasets of cognitive workload. Two of them are collected from n-back tasks and one of them is from finger tapping. From our experiments, we also show the proposed contrastive learning method can also improve baseline models we compared with.

翻译：摘要：功能性近红外光谱成像(fNIRS)是一种非侵入式的大脑皮层血流动力学活动测量技术。基于fNIRS数据预测认知工作负荷的方法已呈现多样化发展趋势。为满足实际应用需求，需要构建能够在不同测试轮次及不同受试者间均表现良好的预测模型。然而现有研究大多假设训练数据与测试数据来自相同受试者，或无法对全新受试者进行有效泛化。fNIRS数据带来的额外挑战包括：受试者间数据高度变异，以及同一受试者不同轮次采集的个体内数据也存在显著差异。针对上述问题，我们提出一种名为"类别感知-分块感知域适应(CABA-DA)"的有效方法，通过将同一受试者同一轮次中的不同数据分块视为独立域，显式最小化同轮次内的方差。该方法在最小化同类域间差异的同时，最大化异类域间差异。此外，我们提出基于MLPMixer的认知负荷分类模型。实验结果表明，与三种基线模型相比，本模型在三个公开认知工作负荷数据集上均取得更优性能。其中两个数据集来源于n-back任务，另一个来源于手指敲击实验。实验进一步证明，所提出的对比学习方法同样能够提升所对比的基线模型性能。