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.

Classifying Driving Fatigue by Using EEG Signals.

Fatigue driving is one of the main reasons for the occurrence of traffic accidents. Brain-computer interface, as a human-computer interaction method based on EEG signals, can communicate with the outside world and move freely through brain signals without relying on the peripheral neuromuscular system. In this paper, a simulation driving platform composed of driving simulation equipment and driving simulation software is used to simulate the real driving process.

Application of a brain-computer interface for person authentication using EEG responses to photo stimuli.

In this paper, a personal authentication system that can effectively identify individuals by generating unique electroencephalogram signal features in response to self-face and non-self-face photos is presented. To achieve performance stability, a sequence of self-face photographs including first-occurrence position and non-first-occurrence position are taken into account in the serial occurrence of visual stimuli. Additionally, a Fisher linear classification method and event-related potential technique for feature analysis is adapted to yield remarkably better outcomes than those obtained by most existing

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.