Fatigue Driving Detection and Early Warning System based on EEG Signals

Abstract

The fatigue driving detection and warning system based on EEG signals aims to use EEG technology to monitor the mental state of drivers in real time, so as to identify and warn fatigue driving behavior in time, so as to improve road safety. The design of the system is based on a deep understanding of the connection between EEG signals and the fatigue state, by analyzing the changes in frequency bands such as α wave, β wave, θ wave and δ wave, which are closely related to the alert state and fatigue in humans. The development of the system first considers the main factors induced by fatigue, such as sleep deprivation, long work hours and environmental monotony, which can significantly affect the activity of brain waves. From these physiological and environmental factors, the system requirements analysis emphasizes the need for real-time data processing and high accuracy, as well as a user interface design that provides clear and intuitive feedback to users (drivers and regulators). In terms of implementation, the system integrates EEG monitoring equipment for data acquisition, uses advanced signal processing algorithm to extract fatigue-related features, and uses machine learning model for real-time analysis and fatigue state determination. In addition, the system design includes an emergency response mechanism, once signs of fatigue are detected, it can automatically trigger an alarm, issue a warning to the driver, and if necessary through the vehicle control system to ensure driving safety. This comprehensive system not only improves driving safety, but also provides strong data support for traffic management departments.