Driver's Hand-Foot Coordination and Global-Regional Brain Functional Connectivity under Fatigue: Via Graph Theory and Explainable Artificial Intelligence

Driving fatigue impairs the driver's hand-foot coordination, a significant factor in traffic accidents. However, the functional brain connectivity underlying driver coordination remains unclear. In this study, we conducted a monotonous long-distance driving experiment (approximately 2 hours) with thirty participants, who rated their fatigue level every 6 minutes using the Karolinska sleepiness scale (KSS). We then categorized the ratings into three fatigue levels: alertness or slight fatigue (AF), moderate fatigue (MF), and severe fatigue (SF). Emergency avoidance scenarios occurred randomly during the experiment, requiring the driver to brake and steer simultaneously to prevent collisions. The multi-band brain functional connectivity network and the critical connections were utilized for global-regional brain functional connectivity analysis and explainable artificial intelligence (XAI) analyses. We found that inter-region connectivity between cognitive and motor regions in the frontal-central and frontal-parietal regions decreased as the driver's fatigue increased to MF. Simultaneously, intra-region connectivity within the central region decreased, impairing intra-motor region connectivity. These diminished the driver's coordination ability. However, profound fatigue triggered an inter-regional and intra-regional reorganization of the brain functional network, reflecting anti-fatigue characteristics but insufficient to enhance driving performance. The XAI results indicated that the beta-band brain functional connection network dataset achieved the highest accuracy (0.941) on the Resnet18 model and revealed fatigue differences in the cognitive and motor regions of the dataset. Our study demonstrated that the driver's hand-foot coordination had distinct brain functional connectivity characteristics under different fatigue states, which could inform the design of neural network detection algorithms for driving fatigue.