Investigating Driver Fatigue versus Alertness Using the Granger Causality Network
DOI: 10.3390/s150819181
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Summary
This study addresses the critical safety issue of driver fatigue, a leading cause of traffic accidents worldwide, by investigating whether changes in brain network topology can serve as reliable neurometric indicators for detecting drowsiness. While previous research has focused on behavioral changes or isolated physiological signals, this work aims to quantify the brain’s ability to integrate information during the transition from an alert to a drowsy state. The authors specifically sought to identify differences in electroencephalogram (EEG) patterns and determine which cortical regions are most affected by fatigue, thereby providing a foundation for future fatigue detection systems. To achieve this, twelve healthy young subjects participated in a simulated driving experiment conducted between 6 p.m. and 9 p.m. to induce natural sleepiness. The protocol included baseline recordings and eight driving conditions, ranging from performance-based tasks to monotonous driving designed to induce drowsiness. Scalp EEG signals were recorded using a 16-channel system, with data from the initial "Warm-Up" (alert) and final "Drowsy" conditions selected for analysis. The researchers applied spectral Granger Causality (GC) to estimate effective connectivity between EEG channels across delta, theta, alpha, and beta frequency bands. Network properties, including global efficiency, characteristic path length, causal flow, and causal density, were calculated using graph theory metrics to assess the structural and functional changes in the brain networks. The results demonstrated significant differences between alert and drowsy states. Granger causality strength increased significantly in the theta and alpha bands during drowsiness, indicating enhanced short-range connections. Conversely, global efficiency and characteristic path length decreased significantly in the delta and theta bands, reflecting a reduced ability of the brain to integrate information globally. The percentage of unconnected nodes increased in the delta, theta, and alpha bands, suggesting a breakdown in network connectivity. Furthermore, causal flow decreased over prefrontal, parietal, and posterior midline regions, while increasing over frontal and central lobes. Causal density increased across almost all channels in the delta, theta, and alpha bands, indicating a shift in information processing dynamics. These findings imply that driver fatigue fundamentally alters the topology of brain effective networks, weakening the brain's capacity for global information integration while increasing local causal interactions. The significant changes observed in the frontal and parietal lobes, particularly in the theta and alpha bands, suggest these regions are key markers for fatigue detection. By identifying these specific neurometric indicators, the study provides a scientific basis for developing non-intrusive, EEG-based systems capable of monitoring driver alertness and preventing fatigue-related accidents.
Key finding
Driver fatigue significantly alters brain effective network topology, characterized by increased Granger causality in theta and alpha bands and decreased global efficiency, indicating a reduced ability to integrate information.
Methodology
simulator
Sample size: 12
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. Discovered via topic_sweep_doaj on 2026-06-01.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-06-01 |
| archive | success | openalex | — | — | 5 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-06-01 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: physiological data