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Open AccessArticle
Block-Wise Domain Adaptation for Workload Prediction from fNIRS Data
1
Electrical Engineering and Computer Science Department, Syracuse University, Syracuse, NY 13244, USA
2
Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO 80309, USA
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(12), 3593; https://doi.org/10.3390/s25123593 (registering DOI)
Submission received: 11 April 2025
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Revised: 27 May 2025
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Accepted: 3 June 2025
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Published: 7 June 2025
Abstract
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 n-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.
Share and Cite
MDPI and ACS Style
Wang, J.; Altay, A.; Hirshfield, L.; Velipasalar, S.
Block-Wise Domain Adaptation for Workload Prediction from fNIRS Data. Sensors 2025, 25, 3593.
https://doi.org/10.3390/s25123593
AMA Style
Wang J, Altay A, Hirshfield L, Velipasalar S.
Block-Wise Domain Adaptation for Workload Prediction from fNIRS Data. Sensors. 2025; 25(12):3593.
https://doi.org/10.3390/s25123593
Chicago/Turabian Style
Wang, Jiyang, Ayse Altay, Leanne Hirshfield, and Senem Velipasalar.
2025. "Block-Wise Domain Adaptation for Workload Prediction from fNIRS Data" Sensors 25, no. 12: 3593.
https://doi.org/10.3390/s25123593
APA Style
Wang, J., Altay, A., Hirshfield, L., & Velipasalar, S.
(2025). Block-Wise Domain Adaptation for Workload Prediction from fNIRS Data. Sensors, 25(12), 3593.
https://doi.org/10.3390/s25123593
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