Khan Mehshan Ahmed, Asadi Houshyar, Qazani Mohammad Reza Chalak, Bargshady Ghazal, Oladazimi Sam, Hoang Thuong, Rahimzadeh Ghazal, Najdovski Zoran, Wei Lei, Moradi Hirash, Nahavandi Saeid
Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC 3216, Australia.
College of Science and Engineering, James Cook University, Townsville, QLD 4814, Australia.
Sensors (Basel). 2025 Aug 9;25(16):4921. doi: 10.3390/s25164921.
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.
在先进驾驶辅助系统（ADAS）的支持下，从手动驾驶向有条件自动驾驶的转变带来了挑战，尤其是由于认知过载等人为因素导致碰撞风险增加。驾驶模拟器提供了一个安全且可控的环境，用于在复杂条件下研究这些人为因素。本研究利用功能近红外光谱技术（fNIRS）在具有挑战性的夜间降雨场景中，对真实驾驶模拟器中的认知负荷进行动态评估。38名参与者在驾驶过程中执行听觉n-back任务（0-back、1-back和2-back），模拟多任务需求。对时间序列fNIRS数据应用滑动窗口方法，以捕捉大脑激活的短期波动。使用深度学习模型EEGNet对数据进行分析，采用重叠和非重叠时间分割策略。结果表明，分类性能受学习率和加窗方法的显著影响。值得注意的是，学习率为0.001时性能最高，使用重叠窗口时准确率为100%，使用非重叠窗口时准确率为97%。这些发现凸显了在模拟驾驶场景中结合fNIRS和深度学习进行实时认知负荷监测方面的潜力，并证明了时间建模在生理信号分析中的重要性。
