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In order to effectively test driver fatigue， the surface electromyography (EMG) and electroencephalogram (EEG) were collected in driving processes， and the characteristic parameters were extracted and analyzed combined with biomechanics. The experimental results indicated that the sample entropy (SampEn) and complexity of EMG and EEG gradually decrease with the driving time expends. These characteristic parameters can be reasonably combined by using the principal component analysis. Based on the multiple regression theory， the characteristic parameters at different positions of the body are reasonably combined， and a mathematical model to evaluate fatigued driving is built. The accuracy of the model is up to 95% by the state validation. [ABSTRACT FROM AUTHOR]

Based on the visuo-auditory information of acquaintances and strangers， the possible differences of the cognitive mechanisms were studied by recording EEG signals. Firstly， the ERP signals for different stimuli types were obtained by recording the EEG signals of visuo-auditory stimuli. Then， the directed functional network was constructed by calculating the phase transfer entropy. Finally， network parameters for the key connection were analyzed. The results showthat compared with the directed network induced by unfamiliar information， the role of key nodes in the network of familiar information is strengthened， as well as the aggregation ability. The connections of the familiar network tend to be more global， and the information exchange between different brain regions are increased， which is good for the identification of the familiars. [ABSTRACT FROM AUTHOR]

With high-rise buildings emerging, objective fear of heights detection is a key step in the standardization of the aerial work industry. Taking into account virtual reality, this paper designs an aerial exposure experiment, which studies the brain neural mechanism of fear of heights reaction, and proposes the functional brain network (FBN) to detect the fear of heights .By comparing the basic topological characteristics of FBNs, the brain regions closely related to fear of heights are found through thresholding. By dividing the community structures according to the brain regions, the recognition model of fear of heights is established . The results show that the more severe the fear of heights, the more complicated the FBN. The main brain regions involved in fear of heights include frontal lobe, central area, and occipital lobe. Using these brain regions to divide the community structures, the calculation accuracy of connection strengths on fear of heights can reach (97.37±0.58)%. [ABSTRACT FROM AUTHOR]