Brain Vigilance Analysis Based on the Measure of Complexity / 生物医学工程学杂志

Vigilance is defined as the ability to maintain attention for prolonged periods of time. In order to explore the variation of brain vigilance in work process, we designed addition and subtraction experiment with numbers of three digits to induce the vigilance to change, combined it with psychomotor vigilance task (PVT) to measure this process of electroencephalogram (EEG), extracted and analyzed permutation entropy (PE) of 11 cases of subjects' EEG and made a brief comparison with nonlinear parameter sample entropy (SE). The experimental results showed that: PE could well reflect the dynamic changes of EEG when vigilance decreases, and has advantages of fast arithmetic speed, high noise immunity, and low requirements for EEG length. This can be used as a measure of the brain vigilance indicators.

Non-linear research of alertness levels under sleep deprivation / 生物医学工程学杂志

We applied Lempel-Ziv complexity (LZC) combined with brain electrical activity mapping (BEAM) to study the change of alertness under sleep deprivation in our research. Ten subjects were involved in 36 hours sleep deprivation (SD), during which spontaneous electroencephalogram (EEG) experiments and auditory evoked EEG experiments-Oddball were recorded once every 6 hours. Spontaneous and evoked EEG data were calculated and BEAMs were structured. Results showed that during the 36 hours of SD, alertness could be divided into three stages, i. e. the first 12 hours as the high stage, the middle 12 hours as the rapid decline stage and the last 12 hours as the low stage. During the period SD, LZC of Spontaneous EEG decreased over the whole brain to some extent, but remained consistent with the subjective scales. By BEAMs of event related potential, LZC on frontal cortex decreased, but kept consistent with the behavioral responses. Therefore, LZC can be effective to reflect the change of brain alertness. At the same time LZC could be used as a practical index to monitor real-time alertness because of its simple computation and fast calculation.

Research on individual sleep staging based on principal component analysis and support vector machine / 生物医学工程学杂志

The research of sleep staging is an important basis of evaluating sleep quality and diagnosing diseases. In order to achieve automatic sleep staging, we proposed a new method which combines with principal component analysis (PCA) and support vector machine (SVM) for automatic sleep staging. Firstly, we used PCA to reduce dimension of time-frequency-space domains and nonlinear dynamical characteristics of sleep EEG from 5 subjects to reduce data redundancy. Secondly, we used 1-a-1 SVM to classify sleep stages. The results showed that the correct rate can reach 89.9%, which was better than those of many other similar studies.