Khan, Mehshan Ahmed, Asadi, Houshyar, Chalak Qazani, Mohammad Reza, Bargshady, Ghazal, Oladazimi, Sam, Hoang, Thuong, Rahimzadeh, Ghazal, Najdovski, Zoran, Wei, Lei, Moradi, Hirash, and Saied, Nahavandi (2025) Advancing Cognitive Load Detection in Simulated Driving Scenarios Through Deep Learning and fNIRS Data. Sensors, 25 (16). 4921.

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Abstract

The shift from manual to conditionally automated driving, supported by Advanced Driving Assistance Systems (ADASs), introduces challenges, particularly increased crash risks due to human factors like cognitive overload. Driving simulators provide a safe and controlled setting to study these human factors under complex conditions. This study leverages Functional Near-Infrared Spectroscopy (fNIRS) to dynamically assess cognitive load in a realistic driving simulator during a challenging night-time-rain scenario. Thirty-eight participants performed an auditory n-back task (0-, 1-, and 2-back) while driving, simulating multitasking demands. A sliding window approach was applied to the time-series fNIRS data to capture short-term fluctuations in brain activation. The data were analyzed using EEGNet, a deep learning model, with both overlapping and non-overlapping temporal segmentation strategies. Results revealed that classification performance is significantly influenced by the learning rate and windowing method. Notably, a learning rate of 0.001 yielded the highest performance, with 100% accuracy using overlapping windows and 97% accuracy with non-overlapping windows. These findings highlight the potential of combining fNIRS and deep learning for real-time cognitive load monitoring in simulated driving scenarios and demonstrate the importance of temporal modeling in physiological signal analysis.

Item ID:	86696
Item Type:	Article (Research - C1)
ISSN:	1424-8220
Copyright Information:	© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Funders:	Australian Research Council (ARC)
Projects and Grants:	ARC Project ID: DE210101623
Date Deposited:	09 Sep 2025 23:50
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50% 40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400909 Photonic and electro-optical devices, sensors and systems (excl. communications) @ 30% 52 PSYCHOLOGY > 5204 Cognitive and computational psychology > 520499 Cognitive and computational psychology not elsewhere classified @ 20%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280121 Expanding knowledge in psychology @ 15% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 60% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 25%
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