BRIEF RESEARCH REPORT article
Front. Electron.
Sec. Wearable Electronics
Volume 6 - 2025 | doi: 10.3389/felec.2025.1668332
This article is part of the Research TopicMachine Learning for Operator Fatigue Detection and Monitoring with Wearable ElectronicsView all articles
Effective Connectivity-Based Recognition of Mental Fatigue Patterns Using functional Near-Infrared Spectroscopy
Provisionally accepted
- New York Institute of Technology, Old Westbury, United States
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Mental Fatigue (MF) impairs cognitive performance and alters brain function, yet its underlying neurophysiological mechanisms remain insufficiently understood. While prior functional Near-Infrared Spectroscopy (fNIRS) studies have focused primarily on signal-level changes or undirected connectivity, few have explored how MF modulates causal interactions within cortical networks. In this study, we employed an Effective Connectivity (EC) framework based on generalized partial directed coherence (GPDC) to investigate directional brain dynamics during a cognitively demanding Stroop task. Using a publicly available dataset comprising continuous fNIRS recordings from 21 healthy adults, we modeled EC across six temporal segments to capture the evolving structure of brain networks. Our results revealed a transition from distributed, flexible connectivity patterns to more rigid and stereotyped configurations, particularly within prefrontal and motor regions. These findings were supported by significant changes in EC intensity in key channels over time. Together, our approach highlights the utility of directional connectivity analysis for identifying neural signatures of MF and contributes toward developing more sensitive biomarkers for real-time fatigue monitoring.
Keywords: Mental Fatigue, MF, effective connectivity, EC, pattern recognition, fNIRS
Received: 17 Jul 2025; Accepted: 23 Sep 2025.
Copyright: © 2025 Abdollahpour and Artan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: N. Sertac Artan, nartan@nyit.edu
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