Real Time Classification of Cognitive Load Using fNIRS and EEGNet in a Driving Simulation Task

Understanding and monitoring cognitive workload is essential for ensuring safety and performance in cognitively demanding environments such as driving. In this study, we investigate the effectiveness of Functional Near-Infrared Spectroscopy (fNIRS) in measuring and classifying cognitive workload during a realistic simulated driving task. Participants were required to perform a dual-task paradigm, which combined a primary driving task with a secondary auditory n-back task at three levels of difficulty: 0-back (low workload), 1-back (moderate workload), and 2-back (high workload). This multi-level workload design allowed us to capture a wide range of cognitive demands reflective of real-world multitasking scenarios. To analyze the hemodynamic responses recorded from the prefrontal cortex, we employed the EEGNet deep learning model, adapted for use with fNIRS signals. A Principal Component Analysis (PCA) feature selection method was applied to reduce dimensionality and isolate the most informative features, improving model performance and computational efficiency. We evaluated the model's performance using both overlapping and non-overlapping signal segmentation strategies across different window lengths (10s, 20s, and 30s). Our results show that a learning rate of 0.001 consistently produced the highest classification accuracy. Notably, a 30-second overlapping window achieved 100% accuracy, while 10-second non-overlapping segments yielded 97% accuracy, highlighting the influence of temporal segmentation on workload classification performance.