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Related Experiment Video

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Advancing Cognitive Load Detection in Simulated Driving Scenarios Through Deep Learning and fNIRS Data.

Mehshan Ahmed Khan1, Houshyar Asadi1, Mohammad Reza Chalak Qazani2

  • 1Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC 3216, Australia.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary

Monitoring driver cognitive load using Functional Near-Infrared Spectroscopy (fNIRS) and deep learning in simulators is effective. Optimal learning rates and windowing methods significantly improve accuracy for real-time brain activity assessment.

Keywords:
EEGNetcognitive loaddeep learning modeldriving simulatorfNIRS

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Automotive Engineering

Background:

  • The transition to automated driving systems presents new safety challenges, including increased crash risks from human factors like cognitive overload.
  • Driving simulators offer a controlled environment to study these complex human factors.
  • Advanced Driving Assistance Systems (ADASs) are key to this transition.

Purpose of the Study:

  • To dynamically assess cognitive load in a realistic driving simulator using Functional Near-Infrared Spectroscopy (fNIRS).
  • To evaluate the effectiveness of deep learning models in analyzing fNIRS data for cognitive load monitoring.
  • To investigate the impact of temporal segmentation strategies on classification performance.

Main Methods:

  • Utilized a realistic driving simulator with a challenging night-time-rain scenario.
  • Employed Functional Near-Infrared Spectroscopy (fNIRS) to measure brain activity.
  • Participants engaged in an auditory n-back task to simulate multitasking demands.
  • Applied a sliding window approach to time-series fNIRS data and analyzed it using the EEGNet deep learning model.
  • Compared overlapping and non-overlapping temporal segmentation strategies.

Main Results:

  • Classification performance was significantly influenced by learning rate and windowing method.
  • A learning rate of 0.001 achieved the highest accuracy: 100% with overlapping windows and 97% with non-overlapping windows.
  • The study demonstrated the feasibility of real-time cognitive load monitoring.

Conclusions:

  • Combining fNIRS and deep learning shows significant potential for real-time cognitive load monitoring in simulated driving.
  • Optimizing temporal modeling, including windowing methods and learning rates, is crucial for accurate physiological signal analysis.
  • This approach can inform the design of safer automated driving systems by addressing human factors.