Fatigue Driving Detection and Early Warning System based on EEG Signals

Wang, Luoluo; Zhang, Min; Zhou, Jiexin; Zhong, Wenhuan; Deng, Ruihui; Chen, Shanxin · 2024 · Crossref

DOI: 10.54691/5qj17p77

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the critical safety issue of fatigue driving by proposing a real-time detection and early warning system based on electroencephalogram (EEG) signals. Motivated by the significant risk fatigue poses to road safety, the authors aim to monitor drivers' mental states objectively and provide timely interventions. The study contrasts EEG-based physiological monitoring with other detection methods, such as self-assessment scales, facial feature analysis, and vehicle driving characteristics, highlighting EEG’s ability to directly reflect brain activity changes associated with alertness and fatigue. The system design integrates EEG monitoring equipment for data acquisition with advanced signal processing and machine learning algorithms. The core detection model utilizes a hybrid deep learning architecture combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. The CNN extracts local spatial features from three-second segments of raw EEG data, while the LSTM captures temporal dependencies in the signal sequence. An Squeeze-and-Excitation (SE) module is incorporated to recalibrate feature channels, enhancing the model's representation ability. The system analyzes specific frequency bands—delta, theta, alpha, beta, and gamma waves—to identify fatigue indicators. For instance, increased theta and alpha activity and decreased beta and gamma activity are correlated with sleep deprivation, prolonged work, and monotonic environments. The implementation includes a comprehensive early warning mechanism and database management system. Upon detecting fatigue, the system triggers visual and auditory alarms for the driver. If fatigue persists, it can interface with vehicle control systems to assist in safe deceleration or parking. Additionally, the system records fatigue events and driver responses, providing data for traffic management departments to monitor driver status remotely and analyze safety trends. The authors validated the conceptual framework through a MATLAB simulation, creating a `FatigueDetection` class to simulate fatigue value calculation using a k-Nearest Neighbors (kNN) algorithm, demonstrating the logical flow of data processing and classification. The significance of this work lies in its provision of a non-invasive, objective method for real-time fatigue monitoring, addressing the limitations of subjective self-assessments and environmental dependencies of other methods. By leveraging deep learning to interpret complex EEG patterns, the system offers a robust solution for improving driving safety and supporting regulatory oversight. The authors conclude that widespread adoption of such physiological signal-based systems can significantly reduce traffic accidents caused by fatigue, offering scientific support for traffic management and driver health monitoring.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• drowsiness detection algorithms
• drowsiness
• microsleep
• vigilance
• truck driver fatigue

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: physiological data
• Methodological Resource: tool software