Construction companies suffer huge losses due to labor fatalities and injuries. Since more than 70% of all accidents are related to human activities, detecting and mitigating human-related risks holds the key to improve the safety condition of construction industry. Many research reveals the psychological and emotional conditions of workers could contribute to the fatalities and injuries. More recent observations in the area of neural science and psychology suggest inattentional blindness is one major cause of unexpected human related accidents. Due to the limitation of human mental workload, labors are vulnerable to unexpected hazards while they are focusing on complicated construction tasks. Therefore, detecting the mental conditions of workers could indicate the hazards level of unexpected injuries. However, there is no available measurement can monitor construction workers' mental condition and related hazards. This proposed research aims at proposing a measurement framework to evaluate such hazards through a neural time-frequency analysis approach. At the same time, the researchers also developed a prototype wearable Electroencephalography (EEG) safety helmet to enable the neural information collection.