Research On Safety Signs Identification Via Neuroengineering

After reform and opening up, China's rapid economic development, and people's concerns and needs of safety and health have become more intensive. At the same time, China's industrial accidents and frequent traffic accidents result in huge economic losses and affect social harmony. How to improve people's safety awareness and reduce accidents, has become an issues concerned by government, business and society.Safety signs have been used widely, and traditional industrial engineering also takes a lot of research on safety signs. Recent years, neuro industrial engineering has been rising, and combine traditional industrial engineering and neuroscience, studies the neural response characteristics on various types of signal (visual or auditory) and the appropriate decision-making process of the neural activity. It also can open up brain's black box behind safety signs identification.In this study, the process of safety signs identification as research object, using the experimental method of cognitive neuroscience and event related potential (ERP) as research tools. This research designed a behavioral experiment to determine safety signs arousal and valence. This study also designed two ERP experiments to describe the cognitive process in the intentional and unintentional state. Relevant research conclusions are as follows:1, Propose Dual Bias Model. In the process of safety signs cognition, there are two biases:Negative Attentional Bias and Negative Emotion Bias. Safety signs will cause the receiver the attentional bias (e g attract more attention resources).Then cause the emotion bias and ultimately lead to behavioral overreaction2, P200 reflects input processing-related attention towards affective stimuli. Comparing with non-negative pictures, warning signs elicit higher P2003, LPP was engaged in negative emotional bias, and comparing with non-negative pictures, warning signs elicit higher LPP.4, P200 and LPP amplitude can be used to measure the effectiveness of safety signs. This application will help us to better enhance the design of safety signs...