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Multi-recognition algorithms of human's mental fatigue state based on EEG

机译:基于脑电图的人类心理疲劳状态多识别算法

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In this paper, the EEG was respectively analyzed by the algorithms of power spectrum and wavelet entropy. And both of the algorithms are based on the same experiment. In addition, through the BP neural network, the state of mental fatigue for human being can be analyzed quickly and correctly. By experiment, when the value of δ is higher than before, at the same time, the value of β, α and θ are lower, or the average value of wavelet entropy is lower, the state of mental fatigue can be sure. Finally, the result, wavelet entropy is better in saving time and accuracy, was got from comparing the two algorithms.
机译:在本文中,通过功率谱和小波熵的算法分别分析EEG。 两种算法都基于同一实验。 此外,通过BP神经网络,可以快速且正确地分析人类的心理疲劳状态。 通过实验,当值δ 比以前高,同时,&#x03b2 ;,α 和θ 较低,或者小波熵的平均值较低,精神疲劳的状态可以确定。 最后,结果,从比较两个算法的时间和准确性,小波熵更好。

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