  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.
