Fatigue driving detection technology based on EEG signal
DOI: 10.62051/m0g3gr55
archive: archived pipeline: cataloged verified
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Summary
This review paper addresses the critical safety issue of fatigue driving, which the World Health Organization attributes to 20–30% of road traffic accidents. The authors argue that while facial expression analysis is common, it is susceptible to environmental interference. Consequently, the paper focuses on electroencephalography (EEG) signals as a more objective and precise method for detecting mental fatigue, as they directly reflect brain activity. The study reviews recent advancements in classification algorithms for EEG-based fatigue detection, categorizing them into traditional machine learning and deep learning approaches. The goal is to provide a theoretical foundation for future algorithmic research and highlight the transition from manual feature extraction to automated, deep learning-based solutions. The paper analyzes the technical pipeline of EEG-based detection, which involves signal acquisition, preprocessing (filtering, artifact removal), feature extraction, and classification. It evaluates traditional machine learning methods, such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forests, which typically rely on manually designed features like autoregressive modeling or wavelet transforms. The review also examines deep learning techniques, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Graph Convolutional Networks (GCN), and Generative Adversarial Networks (GAN). These deep learning models are highlighted for their ability to automatically extract complex spatial and temporal features from raw EEG data, thereby reducing human bias and improving generalization. The findings indicate that both traditional and deep learning methods achieve high classification accuracies. Traditional methods, such as SVM combined with Principal Component Analysis, have reached accuracies up to 98%, while hybrid approaches using Flexible Analytic Wavelet Transform achieved 97.9%. Deep learning models demonstrate robust performance in handling complex data; for instance, an RN-CNN model achieved 92.95% accuracy, and a compressed sensing-based LSTM model maintained 95% accuracy even at high compression rates. Notably, a Graph Convolutional Network combined with GANs achieved approximately 96% accuracy and demonstrated strong robustness against environmental noise, maintaining over 90% accuracy at 0 dB signal-to-noise ratios. However, the paper notes challenges such as the vanishing gradient problem in recurrent networks and the high parameter complexity of LSTM models. The significance of this work lies in its comprehensive comparison of algorithmic strategies for fatigue detection. The authors conclude that while deep learning offers superior feature extraction and robustness, challenges regarding portability, real-time performance, and the need for large, standardized datasets remain. The paper emphasizes the necessity for continued research into efficient decoding algorithms and rational experimental paradigms. Future directions include cross-disciplinary collaboration between neuroscience and artificial intelligence, as well as the development of wearable, real-time monitoring systems to enhance public safety and reduce accidents caused by driver fatigue.
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | pdftotext | — | — | 4 | 2026-08-10 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-08-11 |
| verify | success | — | — | — | 1 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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- Empirical Findings: physiological data