Development of an fMRI-compatible driving simulator with simultaneous measurement of physiological and kinematic signals: The multi-biosignal measurement system for driving (MMSD)
DOI: 10.3233/thc-209034
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the lack of integrated systems capable of comprehensively analyzing complex perceptual-motor behaviors, such as driving, by simultaneously measuring central and autonomic nervous system activity alongside vehicle operation metrics. While driving simulators and individual biosignal measurement technologies exist, no prior system allowed for the concurrent acquisition of brain activity, physiological responses, and kinematic data within a functional magnetic resonance imaging (fMRI) environment. The authors aimed to develop and validate the Multi-Biosignal Measurement System for Driving (MMSD), an fMRI-compatible driving simulator designed to enable this simultaneous, multi-modal data collection. The MMSD consists of three primary components: a driving simulator, a physiological measurement system, and a kinematic measurement system. The simulator includes a steering wheel, accelerator, and brake pedal constructed from nonmagnetic Inconel x-750 to minimize magnetic interference, along with a visual system providing virtual reality driving scenarios. The physiological system measures skin conductance level (SCL) and photoplethysmographic (PPG) signals via sensors attached to the participant’s fingers. The kinematic system utilizes a 3-axis accelerometer and a 2-axis gyroscope attached to the participant’s foot to measure acceleration and angular velocity. To ensure fMRI compatibility, all electronic signals are modulated onto optical carriers and transmitted via fiber optics to external computers, isolating the MR room from electromagnetic noise. The system’s feasibility was evaluated by testing its impact on MR image quality and its susceptibility to MR-induced noise. Phantom and human brain images were acquired with and without the MMSD installed, as well as during simulated driving. Results indicated that the MMSD did not blur or deform MR images, with signal-to-noise ratios remaining consistent across all conditions. Furthermore, the main magnetic field, gradient fields, and RF pulses of the MR system did not introduce significant noise into the physiological or kinematic signals. SCL signals remained stable, while PPG signals showed only minor noise increases but remained analyzable. Kinematic data, including ankle acceleration and angular velocity, were recorded accurately during driving maneuvers. The study concludes that the MMSD successfully enables the comprehensive evaluation of driving behavior by quantitatively measuring concurrent brain activity, autonomic nervous system reactions, and human movement. Although the system restricts head movement and natural driving postures due to MR spatial constraints, it provides a robust platform for investigating the neural and physiological correlates of driving. This technology facilitates future research into factors affecting driving performance, such as distraction, fatigue, alcohol impairment, and neuropsychological conditions, offering a detailed view of the complex perceptual-motor processes involved in driving.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | semantic_scholar | — | — | 6 | 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 | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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Information type
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- Empirical Findings: physiological data
- Methodological Resource: tool software, validation psychometrics