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Functional brain network analysis of the fatigue state for coach bus driver

机译:教练总线司机疲劳状态的功能性脑网络分析

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Traffic accident of coach bus occurred frequently in recent years. One important reason of the accident is the fatigue driving of the bus driver. Thus it is very important to monitor and research the state of the driver during the driving process, which can give an evaluation to driver's driving condition and provide a valuable reference to the coach driving. Because there have some different features of human electroencephalogram (EEG) between the fatigue state and the natural state, we used the portable EEG acquisition device, Emotive, to get the EEG of the driver in the natural state (before driving) and the fatigue state (after driving three hours) in the real driving environment. And through the wavelet packet analysis, the EEG was decomposed to four sub frequency band: theta, alpha, beta and delta band, for each band, the nonlinear correlation of different electrode was calculated and functional brain network was constructed. Then the network was converted to binary network by choose an appropriate threshold and some network feature parameters were calculated and compared between the natural state and the fatigue state. The result shows that the differences of the functional brain network between the two states are very clear and this method can be used to monitor and evaluation the driver's fatigue state.
机译:近年来经常发生教练巴士的交通事故。事故的一个重要原因是公交车司机的疲劳驾驶。因此,在驾驶过程中监测和研究驱动器的状态是非常重要的,这可以对驾驶员的驾驶条件进行评估,并为教练驾驶提供有价值的参考。由于疲劳状态和自然状态之间存在一些不同的人体脑电图(EEG)特征,我们使用便携式的EEG采集装置,情绪化,以在自然状态(驾驶前)和疲劳状态下的驾驶员的脑电图(在三个小时后)在真正的驾驶环境中。并且通过小波分组分析,EEG被分解为四个子频带:对于每个频带,θ,α,beta和delta频带,计算不同电极的非线性相关性,并且构建了功能性脑网络。然后,通过选择适当的阈值,通过选择适当的阈值,并计算一些网络特征参数并在自然状态和疲劳状态之间进行比较。结果表明,两种状态之间的功能性大脑网络的差异非常明确,并且这种方法可用于监测和评估驾驶员的疲劳状态。

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