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Significance: We demonstrated the potential of using domain adaptation onfunctional Near-Infrared Spectroscopy (fNIRS) data to classify different levelsof n-back tasks that involve working memory. Aim: Domain shift in fNIRS data isa challenge in the workload level alignment across different experimentsessions and subjects. In order to address this problem, two domain adaptationapproaches -- Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W) wereused. Approach: Specifically, we used labeled data from one session or onesubject to classify trials in another session (within the same subject) oranother subject. We applied G-W for session-by-session alignment and FG-W forsubject-by-subject alignment to fNIRS data acquired during different n-backtask levels. We compared these approaches with three supervised methods:multi-class Support Vector Machine (SVM), Convolutional Neural Network (CNN),and Recurrent Neural Network (RNN). Results: In a sample of six subjects, G-Wresulted in an alignment accuracy of 68 $\pm$ 4 % (weighted mean $\pm$ standarderror) for session-by-session alignment, FG-W resulted in an alignment accuracyof 55 $\pm$ 2 % for subject-by-subject alignment. In each of these cases, 25 %accuracy represents chance. Alignment accuracy results from both G-W and FG-Ware significantly greater than those from SVM, CNN and RNN. We also showed thatremoval of motion artifacts from the fNIRS data plays an important role inimproving alignment performance. Conclusions: Domain adaptation has potentialfor session-by-session and subject-by-subject alignment of mental workload byusing fNIRS data.

Domain Adaptation for Robust Workload Level Alignment Between Sessions and Subjects using fNIRS