Measurement of train driver’s brain activity by functional near-infrared spectroscopy (fNIRS)
DOI: 10.2495/cr060251
archive: archived pipeline: cataloged verified
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Summary
This study addresses the challenge of human error in train operation, which is often exacerbated by the monotonous nature of driving and the resulting decrease in driver arousal. While automation systems like Automatic Train Stop (ATS) and Automatic Train Operation (ATO) exist, they require drivers to maintain high levels of monitoring attention, potentially leading to further arousal reduction. To develop effective driving support systems that account for human behavioral characteristics, the authors argue for a deeper understanding of the relationship between train operation tasks and the driver’s brain activity. The paper specifically investigates the feasibility of measuring this brain activity using functional near-infrared spectroscopy (fNIRS) and proposes a novel analysis method to isolate task-related neural signals from noise. The researchers developed a flexible train-driving simulator comprising a vehicle control unit, a visual system with a projector and screen, and a command computer to simulate realistic operations. Two male participants, proficient in simulator operation but without real train driving experience, performed three runs of a 2.3 km route with three stations. Brain activity was measured using an OMM-3000 near-infrared imaging system, which recorded changes in oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) concentrations. The setup utilized 44 measurement channels, with 22 positioned in the frontal region and 22 in the occipital region. To extract specific brain activity related to driving tasks from the raw fNIRS data, which contains significant noise and physiological artifacts, the authors applied a wavelet-based multi-resolution analysis using Daubechies wavelets. The results demonstrated that train driving activated the brain, evidenced by a general increase in oxy-Hb and a decrease in deoxy-Hb concentrations in both frontal and occipital regions during operation. Specific task-related changes were observed in response to stopping at stations. The multi-resolution analysis successfully decomposed the fNIRS signals, separating low-frequency trends and high-frequency noise (attributed to heartbeat and measurement error) from the task-related components. By reconstructing the signal using specific detail components corresponding to the frequency band of the driving tasks (starting and stopping), the researchers clarified that oxy-Hb concentration peaked during departures from stations. Functional brain imaging of the frontal region further visualized these activations, confirming the method's effectiveness in evaluating driving behavior. The study concludes that fNIRS is a viable, non-invasive tool for monitoring brain function during train operation, offering advantages over fMRI due to its portability and tolerance for movement. The proposed wavelet-based multi-resolution analysis proves effective for isolating task-specific brain activity from complex physiological signals. These findings suggest that such methods can significantly contribute to the development of human-factor-based driving support systems, potentially aiding in the detection of drowsiness or reduced arousal. The authors plan future research to compare these fNIRS results with simultaneous measurements of fMRI, brain waves, and heartbeat to further refine physiological evaluations of train drivers.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | failed | — | — | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | partial | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified_with_issues.
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- Empirical Findings: physiological data