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Updated: Sep 18, 2025

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Block-Wise Domain Adaptation for Workload Prediction from fNIRS Data.

Jiyang Wang1, Ayse Altay1, Leanne Hirshfield2

  • 1Electrical Engineering and Computer Science Department, Syracuse University, Syracuse, NY 13244, USA.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

Predicting cognitive workload using functional near-infrared spectroscopy (fNIRS) is improved by a novel block-wise domain adaptation method. This approach enhances model generalization across subjects and sessions for real-world applications.

Keywords:
cognitivecontrastive learningdomain adaptationfNIRSworkload

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Functional near-infrared spectroscopy (fNIRS) measures cortical hemodynamic activity non-intrusively.
  • Predicting cognitive workload from fNIRS data faces challenges in generalizing across subjects and sessions.
  • Existing methods often fail to perform well on unseen subjects due to high inter-subject and intra-subject data variability.

Purpose of the Study:

  • To develop a robust method for predicting cognitive workload from fNIRS data that generalizes across subjects and sessions.
  • To address the challenges of inter-subject and intra-subject variability in fNIRS data.
  • To improve the applicability of cognitive workload prediction models in real-world settings.

Main Methods:

  • Proposed a block-wise domain adaptation (BWise-DA) method to minimize intra-session variance by treating blocks from the same subject/session as different domains.
  • Minimized intra-class domain discrepancy and maximized inter-class domain discrepancy.
  • Introduced an MLPMixer-based model for workload prediction and a contrastive learning approach.

Main Results:

  • The proposed BWise-DA method and MLPMixer model outperformed three baseline models on three public workload datasets (n-back and finger-tapping tasks).
  • The contrastive learning method improved the performance of baseline models.
  • Visualization confirmed that the models focused on relevant brain regions.

Conclusions:

  • The BWise-DA method effectively enhances the generalization of cognitive workload prediction models using fNIRS data.
  • The MLPMixer-based approach combined with contrastive learning offers a promising direction for accurate and reliable workload assessment.
  • The findings support the use of fNIRS for real-world cognitive workload monitoring.