A Driver Fatigue Detection System Based on EEG Signal

Abstract

The invention provides a driver fatigue detection system based on electroencephalogram signals, which can detect whether the driver is in a fatigue state in real time and quickly, and avoid fatigue driving as much as possible. The system includes three modules: EEG signal acquisition, EEG signal processing and stimulation and result feedback interface. The signal acquisition module collects the driver's EEG signal in real time, and transmits the EEG signal to the signal processing module. The signal processing module completes the signal processing of the EEG signal, including data preprocessing, feature extraction and pattern classification, judges whether the driver is in a fatigue state, and sends the recognition result to the stimulation and result feedback interface module. The stimulation and result feedback interface completes the connection between the system and the computer or smart phone terminal, and displays the recognition result feedback to the driver. Based on the superiority of EEG signals, the present invention has great advantages in the real-time detection of fatigue state, and can greatly reduce the delay of fatigue detection.

Description

technical field [0001] The invention designs a driver fatigue detection system based on electroencephalogram signals. Specifically, the EEG signal of the driver's brain is collected online and sent to the EEG processing module for real-time analysis to determine whether the driver is in a state of fatigue and give feedback signals such as an alarm. The invention belongs to the combined application of the field of cognitive neuroscience and the field of information technology, and belongs to the field of automatic control technology. Background technique [0002] In real life, drivers need to maintain a high degree of attention during driving. Once they are in a state of inattention or fatigue at work, it may cause very serious consequences. For example, according to the US Federal Bureau of Investigation, 20%-30% of traffic accidents are due to fatigue driving. Therefore, the driving fatigue detection system is of great significance to avoid disasters such as traffic accid...

AI Technical Summary

Problems solved by technology

However, this type of method has two problems in practical application: (1) it is greatly affected by individual differences, and is also easily affected by environmental factors such as light; (2) these visual features are relatively easy to camouflage, and the driver can fool the system. detection, so that the system does not measure the real fatigue state of the driver

[0012] One of the challenges in fatigue detection based on EEG signals is the variability of EEG signals

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