Relationship Between Brain Activity and Real-Road Driving Behavior: A Vector-Based Whole-Brain Functional Near-Infrared Spectroscopy Study

Automobile driving requires multiple brain functions. However, the brain regions related to driving behavior are unknown. Therefore, we measured activity of the frontal, parietal and occipital lobes during driving using functional near-infrared spectroscopy (fNIRS). Cortical activation patterns were examined in relation to driving behaviors, such as steering motion, accelerator pedal motion, and speed control. Six healthy adults participated in the experiment. Cerebral oxygen exchange (COE) was calculated based on the oxyhemoglobin and deoxyhemoglobin concentrations measured by fNIRS. The COE and driving behavior data were collected every 1 m and averaged for all subjects. Functional NIRS data for all 98 channels were extracted using principal component analysis. Similarity between extracted components and driving behaviors were confirmed by |cosine similarity|\u3e0.3. Among the factors with confirmed similarity, we identified brain regions with high principal component loading (|PCL|\u3e0.4). Among the 16 COE factors extracted, COE factor 1 and factor 5 exhibited similarity with steering motion (cosine similarity: factor 1, -0.538; factor 5, 0.551). The PCLs of COE factor 1 and factor 5 were high in the frontal lobe (Brodmann areas [BAs] 9, 8, and 4/3) (PCL\u3e0.8). COE factor 6 exhibited a similarity with accelerator pedal motion (cosine similarity: 0.369), and the PCL of COE factor 6 was highest in the parietal lobe (BA7) (PCL= -0.62). Speed control did not exhibit similarity with any COE factor. These findings will contribute to the selection of brain measurement areas when fNIRS is used for vehicle driving assessment

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

Towards multimodal driver state monitoring systems for highly automated driving

Real-time monitoring of drivers’ functional states will soon become a required safety feature for commercially available vehicles with automated driving capability. Automated driving technology aims to mitigate human error from road transport with the progressive automatisation of specific driving tasks. However, while control of the driving task remains shared between humans and automated systems, the inclusion of this new technology is not exempt from other human factors-related challenges. Drivers’ functional states are essentially a combination of psychological, emotional, and cognitive states, and they generate a constant activity footprint available for measurement through neural and peripheral physiology, among other measures. These factors can determine drivers’ functional states and, thus, drivers’ availability to safely perform control transitions between human and vehicle. This doctoral project aims at investigating the potential of electrocardiogram (ECG), electrodermal activity (EDA) and functional near-infrared spectroscopy (fNIRS) as measures for a multimodal driver state monitoring (DSM) system for highly automated driving (i.e., SAE levels 3 and 4). While current DSM systems relying on gaze behaviour measures have proven valid and effective, several limitations and challenges could only be overcome using eye-tracking in tandem with physiological parameters. This thesis investigates whether ECG, EDA and fNIRS would be good candidates for such a purpose. Two driving simulator studies were performed to measure mental workload, trust in automation, stress and perceived risk, all identified as modulators of drivers’ functional states and that could eventually determine drivers’ availability to take-over manual control. The main findings demonstrate that DSM systems should adopt multiple physiological measures to capture changes in functional states relevant for driver readiness. Future DSM systems will benefit from the knowledge generated by this research by applying machine learning methods to these measures for determining drivers’ availability for optimal take-over performance

Proceedings of the 3rd International Mobile Brain/Body Imaging Conference : Berlin, July 12th to July 14th 2018

The 3rd International Mobile Brain/Body Imaging (MoBI) conference in Berlin 2018 brought together researchers from various disciplines interested in understanding the human brain in its natural environment and during active behavior. MoBI is a new imaging modality, employing mobile brain imaging methods like the electroencephalogram (EEG) or near infrared spectroscopy (NIRS) synchronized to motion capture and other data streams to investigate brain activity while participants actively move in and interact with their environment. Mobile Brain / Body Imaging allows to investigate brain dynamics accompanying more natural cognitive and affective processes as it allows the human to interact with the environment without restriction regarding physical movement. Overcoming the movement restrictions of established imaging modalities like functional magnetic resonance tomography (MRI), MoBI can provide new insights into the human brain function in mobile participants. This imaging approach will lead to new insights into the brain functions underlying active behavior and the impact of behavior on brain dynamics and vice versa, it can be used for the development of more robust human-machine interfaces as well as state assessment in mobile humans.DFG, GR2627/10-1, 3rd International MoBI Conference 201

Physiological Characteristics and Nonparametric Test for Master-Slave Driving Task’s Mental Workload Evaluation in Mountain Area Highway at Night

With the rapid development of advanced mobile intelligent terminals, driving tasks are diverse, and new traffic safety problems occur. We propose a new research on physiological characteristics and nonparametric tests for the master-slave driving task, especially for evaluation of drivers’ mental workload in mountain area highway in nighttime scenario. First, we establish the experimental platform based driving simulator and design the master-slave driving task. Second, based on the physiological data and subjective evaluation for mental workload, we use statistical methods to composite the physical changes evolution analysis in a driving simulator. Finally, we finished nonparametric test of the drivers’ psychological load and road test. The results show that in compassion with the daytime scenario, drivers should pay much effort to driving skills and risk identification in the nighttime scenario. Thus, in the same driving condition, drivers should bear the higher level of mental workload, and it has been subjected to even greater pressures and intensity of emotions. Document type: Articl

Psychophysiological models of hypovigilance detection: A scoping review

Hypovigilance represents a major contributor to accidents. In operational contexts, the burden of monitoring/managing vigilance often rests on operators. Recent advances in sensing technologies allow for the development of psychophysiology‐based (hypo)vigilance prediction models. Still, these models remain scarcely applied to operational situations and need better understanding. The current scoping review provides a state of knowledge regarding psychophysiological models of hypovigilance detection. Records evaluating vigilance measuring tools with gold standard comparisons and hypovigilance prediction performances were extracted from MEDLINE, PsychInfo, and Inspec. Exclusion criteria comprised aspects related to language, non‐empirical papers, and sleep studies. The Quality Assessment tool for Diagnostic Accuracy Studies (QUADAS) and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were used for bias evaluation. Twenty‐one records were reviewed. They were mainly characterized by participant selection and analysis biases. Papers predominantly focused on driving and employed several common psychophysiological techniques. Yet, prediction methods and gold standards varied widely. Overall, we outline the main strategies used to assess hypovigilance, their principal limitations, and we discuss applications of these models

Psychologie und Gehirn 2007

Die Fachtagung "Psychologie und Gehirn" ist eine traditionelle Tagung aus dem Bereich psychophysiologischer Grundlagenforschung. 2007 fand diese Veranstaltung, die 33. Jahrestagung der „Deutschen Gesellschaft für Psychophysiologie und ihre Anwendungen (DGPA)“, in Dortmund unter der Schirmherrschaft des Instituts für Arbeitsphysiologie (IfADo) statt. Neben der Grundlagenforschung ist auch die Umsetzung in die Anwendung erklärtes Ziel der DGPA und dieser Tradition folgend wurden Beiträge aus vielen Bereichen moderner Neurowissenschaft (Elektrophysiologie, bildgebende Verfahren, Peripherphysiologie, Neuroendokrinologie, Verhaltensgenetik, u.a.) präsentiert und liegen hier in Kurzform vor

Simulator based human performance assessment in a ship engine room using functional near-infrared spectroscopy

80% of accidents that occur in the maritime sector are due to human error. These errors could be the result of seafarer training coupled with a high mental workload due to the addition of various working conditions. The aim of this study is to evaluate the effect of various stressors on human performance on engine room operations. To achieve this aim, a simulator study was conducted to investigate the influence of training and working conditions on human performance for the purposes of fault detection and correction in a maritime engine room. 20 participants were recruited for each investigation of performance shaping factors (PSF); for the first test, half received practical training with the engine room software interface, while the other half were provided with paper-based instructions. The remaining tests were conducted with all participants equally practically trained. The participants interacted with a TRANSAS technological simulator series 5000. This is a 1:1 simulation of a ship engine room. The participants took part in a 30-minute scenario where they had to detect and correct a fault with the ballasting system. During this interaction, half of the participants experienced simulated, adverse performance shaping factors, which were distraction, fatigue and an increased workload. The other half were given a standard task. Functional near-infrared spectroscopy (fNIRS) was utilised to measure neurophysiological activation from the dorsolateral prefrontal cortex (DLPFC). The results indicated increased activation of lateral regions of the DLPFC during fault correction, this trend was enhanced due to PSF’s and training, i.e. participants who received paper-based instructions showed greater activation when conducting the standard task and had an exponential increase in activation when dealing with the addition of an adverse PSF. The results are discussed with respect to the neural efficiency of the operator during high mental workload. From the results of this study a scientific human error model was developed and can be used by the maritime industry to better evaluate and understand human error causation and the effect of PSF on seafarers. The impact of this study could reduce the frequency of occurrence of human error, reduce the financial impact that human error has on the maritime sector and reduce injuries and fatalities