It is an important means to study the electrical activity of the brain's nerve cells by exploring physiological information of the EEGs from the frequency domain. The gravity frequency is one of the global parameters with using this method. We used the multitaper spectrum method (MTM) spectrum estimation method of good performance to calculate the EEG spectrum and its gravity frequency of subjects under vigilance and vigilance decrement state. The results showed that the gravity frequency of vigilance state was higher than that of vigilance decrement state, the gravity frequency became smaller along with the vigilance decrement, and the location of the gravity frequency shifted to the left in the spectrum. Finally, the monitoring curve of the gravity frequency was acquired by designing an algorithm, and it was used to online monitoring vigilance operators.
Poor and monotonous work could easily lead to a decrease of arousal level of the monitoring work personnel. In order to improve the performance of monitoring work, low arousal level needs to be recognized and awakened. We proposed a recognition method of low arousal by the electroencephalogram (EEG) as the object of study to recognize the low arousal level in the vigilance. We used wavelet packet transform to decompose the EEG signal so the EEG rhythms of each component were obtained, and then we calculated the parameters of relative energy and energy ratio of high-low frequency, and constructed the feature vector to monitor low arousal state in the operation. We finally used support vector machine (SVM) to recognize the low arousal state in the simulate operation. The experimental results showed that the method introduced in this article could well distinguish low arousal level from arousal level in the vigilance and it could also get a high recognition rate. Have been compared with other analysis methods, the present method could more effectively recognize low arousal level and provide better technical support for wake-up mechanism of low arousal state.