Applications of brain imaging methods in driving behaviour research

Applications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of certain types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. Different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semi-automated settings or the brain activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Further, potential topics in relation to driving behaviour are identified that could benefit from the adoption of neuroimaging methods in future studies.

💡 Research Summary

The paper presents a comprehensive review of how neuroimaging techniques have been employed to investigate driver brain activity and, consequently, driving behaviour. Four major modalities are covered: functional magnetic resonance imaging (fMRI), electroencephalography (EEG), functional near‑infrared spectroscopy (fNIRS), and magnetoencephalography (MEG). The authors first outline methodological considerations, distinguishing between simulator‑based experiments and the few field studies that have managed to collect usable brain signals under real‑world conditions. They then compare each modality in terms of spatial and temporal resolution, portability, cost, and susceptibility to artefacts. fMRI offers the finest spatial detail, allowing researchers to map activity in prefrontal, parietal and occipital cortices during intoxicated or distracted driving. Its main drawback is the need for a stationary, noisy scanner, which limits ecological validity. EEG provides millisecond‑scale temporal resolution, making it ideal for detecting the gradual rise of alpha and theta power that accompanies fatigue and drowsiness; this has driven a surge of work on automatic fatigue‑detection algorithms that increasingly rely on machine‑learning classifiers. However, EEG signals are vulnerable to motion artefacts and require sophisticated preprocessing. fNIRS measures cortical haemodynamics through near‑infrared light, offering a good compromise between portability and signal quality; it has been used to track workload‑related changes in prefrontal oxygenation during simulated driving. Its depth penetration is shallow, restricting observations to surface cortex. MEG delivers both high temporal and spatial precision but is expensive and confined to magnetically shielded rooms, which explains its limited use in driving research.

The review identifies four thematic clusters in the literature. The first cluster focuses on substance‑related impairment (alcohol, cannabis) and uses fMRI to demonstrate reduced inhibitory control in the prefrontal cortex and altered risk‑assessment networks. The second cluster examines distraction (mobile phone, navigation) and often combines fMRI with EEG to capture the rapid reallocation of attention across visual and auditory modalities, highlighting transient deactivations in the dorsal attention network. The third cluster, the most prolific, concerns fatigue and drowsiness detection; EEG studies dominate this area, reporting characteristic increases in low‑frequency power, reductions in P300 amplitude, and successful classification of alert versus sleepy states using support‑vector machines, convolutional neural networks, or ensemble methods. Recent work even integrates fNIRS‑derived haemodynamic markers to improve robustness. The fourth cluster explores workload and stress, primarily with fNIRS, showing linear relationships between task difficulty and prefrontal blood‑oxygen‑level‑dependent signals.

Notably, the authors point out substantial gaps. Research on semi‑automated (Level 2–3) driving, where the driver must monitor and intervene, is scarce; likewise, studies involving drivers with brain injuries, neurodegenerative diseases, or chronic neurological conditions are almost nonexistent. Multimodal investigations that fuse fMRI with EEG, or fNIRS with EEG, remain in their infancy, limiting our ability to capture the full spatiotemporal dynamics of driver cognition.

To address these shortcomings, the paper proposes several future directions. First, the development of mobile, high‑density EEG and fNIRS systems capable of reliable data acquisition in real traffic, together with advanced artefact‑rejection pipelines, will enhance ecological validity. Second, establishing standardized protocols for multimodal data collection and creating open‑access repositories will facilitate cross‑study comparisons and meta‑analyses. Third, ethical frameworks that protect driver privacy while allowing data sharing must be instituted. Fourth, dedicated investigations into driver–automation interaction, especially in the context of takeover requests, should leverage combined neuroimaging to uncover the neural signatures of situational awareness, mental workload, and decision latency. Finally, integrating neuroimaging outcomes with vehicle control systems could enable adaptive interfaces that respond to the driver’s cognitive state in real time, thereby improving safety and user experience. In sum, the review underscores that while neuroimaging has already yielded valuable insights into intoxication, distraction, and fatigue, a broader, more integrated application of these tools holds great promise for advancing human‑centred vehicle design and road safety.