Toward early warning of unsafe behavior of excavator operators under time pressure: experimental evidence and EEG-based detection via RCF-IncepLite model.

Time pressure can impair the cognitive functioning of excavator operators, thereby increasing unsafe behaviors and elevating the likelihood of accidents. This study uses a controlled excavator operation task with synchronized behavioral observation and electroencephalography (EEG) recording to examine how escalating time pressure alters operators' cognitive states and safety performance. Results show that as the task deadline approaches, the frequency of unsafe behaviors increases significantly, accompanied by heightened beta-band power and elevated engagement index, reflecting potential cognitive overload under time pressure. To facilitate timely identification of these risk-related neural patterns, we develop RCF-IncepLite, a lightweight EEG-based classification model designed for resource-constrained environments. The model achieves 82.3% accuracy while maintaining minimal computational demands, underscoring its potential for future integration into wearable neuro-sensing systems for early warning of unsafe behaviors. This study provides empirical evidence of the cognitive pathways through which time pressure elevates behavioral risk in construction, and offers a practical methodological foundation for advancing proactive accident prevention in fast-paced construction environments.