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FFTNet: fNIRS-based frequency-enhanced patch network for driving fatigue detection.

Yu Li1, Xudong Jia2, Yu Sun3

  • 1School of Electronics and Information Engineering, Wuyi University, Jiangmen, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using functional near-infrared spectroscopy (fNIRS) and deep learning to accurately detect driving fatigue by analyzing both temporal and frequency-domain brain signals.

Keywords:
Driving fatigue detectionFourier domainPatch embeddingPatch mergingfNIRS

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Driving fatigue poses significant safety risks.
  • Functional near-infrared spectroscopy (fNIRS) offers a portable and non-invasive method for monitoring brain activity.
  • Deep learning models for fNIRS data often struggle to capture both local temporal details and global dependencies, and typically overlook frequency-domain information.

Purpose of the Study:

  • To develop an advanced fNIRS signal modeling method for enhanced driving fatigue detection.
  • To effectively integrate temporal and frequency-domain features for improved accuracy and robustness.
  • To investigate the neural mechanisms underlying driving fatigue through brain activation and functional connectivity analyses.

Main Methods:

  • Proposed a novel fNIRS signal modeling method incorporating structured encoding and frequency-domain feature enhancement.
  • Employed Patch Embedding and Patch Merging for multi-scale temporal feature extraction.
  • Introduced a learnable Fourier-based frequency weighting mechanism and fused temporal and frequency-domain features.
  • Conducted brain activation and functional connectivity analyses to explore neural mechanisms.

Main Results:

  • The proposed method significantly improved the accuracy and robustness of driving fatigue state recognition.
  • Visualization of frequency-domain weights confirmed the model's ability to capture personalized and global spectral features.
  • Brain activation patterns revealed neural resource depletion (negative HbO) and compensatory mechanisms (positive HbR) during fatigue.
  • Functional connectivity analysis indicated enhanced HbO integration and reduced HbR synchronization under fatigue.

Conclusions:

  • The integrated temporal and frequency-domain fNIRS modeling approach enhances driving fatigue detection.
  • Driving fatigue involves complex neurophysiological regulation, including functional compensation and dysregulation.
  • This study provides comprehensive insights into how driving fatigue impacts brain function and connectivity.