Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals.

The objective of this article is to solve the current social phenomenon of a large number of fatigue driving, so that social safety becomes more stable in the future, and the detection and application of driving fatigue are more meaningful. This article aims to study the application of graph neural network (GNN) in driving fatigue detection (this article is abbreviated as DFD) based on EEG signals. This article uses a pattern classification method based on a multilayer perceptual overlimit learning machine to find the hidden information of the signal through an unsupervised learning self-encoding structure, which achieves the optimization purpose and has a better classification effect than traditional classifiers. An improved soft threshold (the soft threshold can be used to solve the optimization problem, and the optimization problem solved is similar to the base pursuit noise reduction problem, but it is not the same, and it should be noted that the soft threshold cannot solve the base pursuit noise reduction problem) denoising algorithm is selected, and the collected EEG (a technique for capturing brain activity using electrophysiological markers is the electroencephalogram). The sum of the postsynaptic potentials produced simultaneously by a large number of neurons occurs when the brain is active. It records the process of brain activity in the cerebral cortex or scalp surface) signals are preprocessed, so that the feature extraction efficiency of extracting EEG signals is improved. The final experimental data show that the traditional support vector machine, SVM algorithm, and the KNN convolutional neural (the K-nearest neighbor method, often known as KNN, was first put forth by Cover and Hart in 1968. It is one of the most straightforward machine learning algorithms and a theoretically sound approach) algorithms has a recognition rate of 79% and 81% for fatigue. The improved algorithm in this article has an average recognition rate of 87.5% for driver fatigue, which is greatly improved.

Design of electroencephalogram authentication access control to smart car.

In recent years, with the development of intelligent vehicles, the demand for security will be bigger and bigger. One of the most important solutions is the use of new biometric technology. At present, there are still some areas to be improved on biometric technology. For example, diseases will destroy some biological characteristics, some detection methods are too slow, many detection methods do not need living detection, and so on. Electroencephalogram (EEG) is a new biometric tool for living identification. In this Letter, a kind of identity authentication system based on the EEG signal is presented. The overall goal of this research is to design a new authentication method and develop the corresponding application. Therefore, the authors carried out a series of EEG experiments, and analysed and discussed the experimental results. Based on these results, they build and present an access control system based on the uniqueness of their EEG signals to be capable of authenticating access control to the car. The accuracy of the authentication system is >87.3%.

Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals.

The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors' main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles. (ii) An android application for fatigue detection is built. The application can be used for the driver to detect the state of his/her fatigue based on EEG signals, and warn neighbourhood vehicles. (iii) The detection algorithm for driver fatigue is applied based on fuzzy entropy. The idea of 10-fold cross-validation and support vector machine are used for classified calculation. Experimental results show that the average accurate rate of detecting driver fatigue is about 95%, which implying that the algorithm is validity in detecting state of driver fatigue.

Relation between plasma oxLDL antibodies and oxLDL in the circulation.

OBJECTIVES: To investigate the possible relationship between circulating oxLDL and oxLDL Ab concentrations in order to better understand the role of oxLDL Ab in atherosclerosis. DESIGN: In a cross-sectional study of 86 patients undergoing ultrasonographic scanning of the carotid artery, concentration of circulating oxLDL and oxLDL Ab were determined by ELISA. For multivariate analysis, we also measured many conventional cardiovascular risk factors. Spearman's rank correlation test and chi-square test were used to evaluate the correlation between circulating oxLDL and oxLDL Ab. A stepwise multiple regression analysis and Logistic regression analysis were used to study the influencing factors. RESULTS: There was no linear correlation between circulating oxLDL and oxLDL Ab. Circulating oxLDL was influenced by HDL, systolic blood pressure and body mass index, while only TC was independent factor associating with oxLDL Ab. CONCLUSIONS: The present study showed that oxLDL was present at a low concentration in the circulation, and that plasma oxLDL had no linear correlation with oxLDL antibodies. However, the issue still needs further study.