Prefrontal Cortex Activation and Young Driver Behaviour: A fNIRS Study

An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios

For highly autonomous vehicles, human does not need to operate continuously vehicles. The brain-computer interface system in autonomous vehicles will highly depend on the brain states of passengers rather than those of human drivers. It is a meaningful and vital choice to translate the mental activities of human beings, essentially playing the role of advanced sensors, into safe driving. Quantifying the driving risk cognition of passengers is a basic step toward this end. This study reports the creation of an fNIRS dataset focusing on the prefrontal cortex activity in fourteen types of highly automated driving scenarios. This dataset considers age, sex and driving experience factors and contains the data collected from an 8-channel fNIRS device and the data of driving scenarios. The dataset provides data support for distinguishing the driving risk in highly automated driving scenarios via brain-computer interface systems, and it also provides the possibility of preventing potential hazards in some scenarios, in which risk remains at a high value for an extended period, before hazard occurs.

How a pilot's brain copes with stress and mental load? Insights from the executive control network

In aviation, mental workload and stress are two major factors that can considerably impact a pilot's flight performance and decisions. Their consequences can be even more dramatic in single-pilot aircraft or with the forthcoming single-pilot operations where the pilot will fly alone and will not be able to be assisted in case of difficulty. An accurate and automatic monitoring of the pilot's mental state could help to prevent the potentially dangerous effects of an excess mental workload and stress. For example, some tasks could be allocated to automation or to a ground-based flight crew if a mental overload or significant stress is detected. In the current study, the brain activity of 20 private pilots was recorded with a fNIRS device during two realistic flight simulator scenarios. The mental workload was manipulated with the added difficulty of a secondary task and stress was induced by a social stressor. Our results confirmed the sensitivity of the fNIRS readings to variations in the mental workload, with increased HbO2 concentration in regions of the executive control network (ECN), in particular in the dorsolateral prefrontal cortex and in lateral parietal regions, when the difficulty of the secondary task was high. The social stressor also triggered an HbO2 increase in the ECN, especially when it was combined with high mental workload. This latter result suggests that mental workload and stress together can have cumulative effects, and coping with both factors is possible at the expense of an extra recruitment of the ECN. Finally, results also revealed a time-on-task effect, with a progressive reduction of the HbO2 signal in the ECN during the flight scenario, suggesting that these regions are sensitive to short term habituation to the tasks. Overall, fNIRS efficiently indexed mental load, stress, and practice effects.

A systematic review on the influence factors, measurement, and effect of driver workload

Driver workload (DWL) is an important factor that needs to be considered in the study of traffic safety. The research focus on DWL has undergone certain shifts with the rapid development of scientific and technological advancements in the field of transportation in recent years. This study aims to grasp the state of research on DWL by both bibliometric analysis and individual critical literature review. The knowledge structure and development trend are described using bibliometric analysis. The knowledge mapping method is applied to mine the available literature in depth. It is discovered that one of the current research focus on DWL has shifted towards investigating its application in the field of autonomous driving. Subjective questionnaires and experimental tests (including both simulation technology and field study) are the main approaches to analyze DWL. An individual critical literature review of the influencing factors, measurement, and performance of DWL is provided. Research findings have shown that DWL was highly impacted by both intrinsic (e.g., age, temperament, driving experience) and external factors (e.g., vehicles, roads, tasks, environments). Scholars are actively exploring the combined effects of various factors and the level of vehicle automation on DWL. In addition to assess DWL by using subjective measures or physiological parameter measures separately, studies have started to improve classification accuracy by combining multiple measurement methods. Safety thresholds of DWL are not sufficiently studied due to the various interference items corresponding to different scenarios, but it is expected to quantify the DWL and find the threshold by establishing assessment models considering these intrinsic and external multiple-factors simultaneously. Driver or vehicle performance indicators are controversial to measure DWL directly, but they were suitable to reflect the impact of DWL in different driving conditions.

The Effectiveness of a Digital Game-Based Intervention on Hazard Perception and Visual Skills in Novice Drivers: A Single Blind, Randomized Controlled Trial

Novice drivers show poorer performance than experienced drivers in terms of visual skills and hazard perception. This study aimed at evaluating the effectiveness of a digital game-based intervention on hazard perception and visual skills in novice drivers. Forty-six novice drivers (6 men, 40 women) were randomized to the intervention group (n = 23; 20.79 ± 0.81 years) or control (n = 23; 20.65 ± 0.93 years) group. The intervention group received a game-based intervention in addition to a hazard perception training, whereas the control group received only the hazard perception training. Hazard perception and visual skills were assessed in both groups before and after the 14-day interventions. Between-group comparisons revealed significantly greater improvements in visual short time memory, visual closure, visual discrimination, figure-ground and total scores in the game-based group than in the control group (p < 0.05 for all). Our results showed that 14 days of game-based intervention enhanced hazard perception and visual skills in novice drivers. Using game-based interventions in driving rehabilitation is recommended to improve hazard perception and visual skills of novice drivers.

Carbon dioxide protects simulated driving performance during severe hypoxia

PURPOSE

We sought to determine the effect of acute severe hypoxia, with and without concurrent manipulation of carbon dioxide (CO₂), on complex real-world psychomotor task performance.

METHODS

Twenty-one participants completed a 10-min simulated driving task while breathing room air (normoxia) or hypoxic air (P_ETO₂ = 45 mmHg) under poikilocapnic, isocapnic, and hypercapnic conditions (P_ETCO₂ = not manipulated, clamped at baseline, and clamped at baseline + 10 mmHg, respectively). Driving performance was assessed using a fixed-base motor vehicle simulator. Oxygenation in the frontal cortex was measured using functional near-infrared spectroscopy.

RESULTS

Speed limit exceedances were greater during the poikilocapnic than normoxic, hypercapnic, and isocapnic conditions (mean exceedances: 8, 4, 5, and 7, respectively; all p ≤ 0.05 vs poikilocapnic hypoxia). Vehicle speed was greater in the poikilocapnic than normoxic and hypercapnic conditions (mean difference: 0.35 km h^-1 and 0.67 km h^-1, respectively). All hypoxic conditions similarly decreased cerebral oxyhaemoglobin and increased deoxyhaemoglobin, compared to normoxic baseline, while total hemoglobin remained unchanged.

CONCLUSIONS

These findings demonstrate that supplemental CO₂ can confer a neuroprotective effect by offsetting impairments in complex psychomotor task performance evoked by severe poikilocapnic hypoxia; however, differences in performance are unlikely to be linked to measurable differences in cerebral oxygenation.

Effects of Personalized Cognitive Training with the Machine Learning Algorithm on Neural Efficiency in Healthy Younger Adults

To date, neural efficiency, an ability to economically utilize mental resources, has not been investigated after cognitive training. The purpose of this study was to provide customized cognitive training and confirm its effect on neural efficiency by investigating prefrontal cortex (PFC) activity using functional near-infrared spectroscopy (fNIRS). Before training, a prediction algorithm based on the PFC activity with logistic regression was used to predict the customized difficulty level with 86% accuracy by collecting data when subjects performed four kinds of cognitive tasks. In the next step, the intervention study was designed using one pre-posttest group. Thirteen healthy adults participated in the virtual reality (VR)-based spatial cognitive training, which was conducted four times a week for 30 min for three weeks with customized difficulty levels for each session. To measure its effect, the trail-making test (TMT) and hemodynamic responses were measured for executive function and PFC activity. During the training, VR-based spatial cognitive performance was improved, and hemodynamic values were gradually increased as the training sessions progressed. In addition, after the training, the performance on the trail-making task (TMT) demonstrated a statistically significant improvement, and there was a statistically significant decrease in the PFC activity. The improved performance on the TMT coupled with the decreased PFC activity could be regarded as training-induced neural efficiency. These results suggested that personalized cognitive training could be effective in improving executive function and neural efficiency.

Ecological validity of executive function tests in predicting driving performance

Almost all of our everyday activities depend on executive function (EF) skills. In line with the increasing attention to the ecological validation of neuropsychological assessment and intervention methods, this study aimed to explore the ecological validity of a relevant set of widely used EF tests, mostly from well-known paradigms of EF assessment, in predicting driving ability. Ninety-six healthy novice drivers (Mage = 26.2 years, SD = 8.4; 48 female) completed four stages of our data collection including psychological, EF, and driving assessments. For the psychological assessment, validated measures of sensation-seeking, risk-taking, personality traits, ADHD symptoms, depression, anxiety, and stress were administered. For the EF assessment, selected tests from the Delis-Kaplan Executive Function System (D-KEFS: Trail Making, Design Fluency, and Tower) and the Behavioral Assessment of the Dysexecutive Syndrome (BADS: Key Search, Zoo Map, and Modified Six Elements) along with a computerized Stroop test were administered. For the driving assessment, we used a simulated driving test comprising of 14 key dimensions of driving skills. Several correlations and multiple regression analyses were conducted. Significant correlations were found between all the EF measures and driving performance. Moreover, the EF measures predicted the driving ability over and above the effects of previous driving experience and the psychological variables. These results provide supporting evidence for the ecological validity of the EF tests in predicting driving performance. The incorporation of assessment and intervention targeting multiple domains of EF into driving rehabilitation and education programs could be a focus of future research.

Driver's turning intent recognition model based on brain activation and contextual information

Traffic situations like turning at intersections are destined for safety-critical situations and accidents. Human errors are one of the main reasons for accidents in these situations. A model that recognizes the driver's turning intent could help to reduce accidents by warning the driver or stopping the vehicle before a dangerous turning maneuver. Most models that aim at predicting the probability of a driver's turning intent use only contextual information, such as gap size or waiting time. The objective of this study is to investigate whether the combination of context information and brain activation measurements enhances the recognition of turning intent. We conducted a driving simulator study while simultaneously measuring brain activation using high-density fNIRS. A neural network model for turning intent recognition was trained on the fNIRS and contextual data. The input variables were analyzed using SHAP (SHapley Additive exPlanations) feature importance analysis to show the positive effect of the inclusion of brain activation data. Both the model's evaluation and the feature importance analysis suggest that the combination of context information and brain activation leads to an improved turning intent recognition. The fNIRS results showed increased brain activation differences during the "turn" decision-making phase before turning execution in parts of the left motor cortices, such as the primary motor cortex (PMC; putative BA 4), premotor area (PMA; putative BA 6), and supplementary motor area (SMA; putative BA 8). Furthermore, we also observed increased activation differences in the left prefrontal areas, potentially in the left middle frontal gyrus (putative BA 9), which has been associated with the control of executive functions, such as decision-making and action planning. We hypothesize that brain activation measurements could be a more direct indicator with potentially high specificity for the turning behavior and thus help to increase the recognition model's performance.

Evaluating mental workload during multitasking in simulated flight

BACKGROUND

Pilots must process multiple streams of information simultaneously. Mental workload is one of the main issues in man-machine interactive mode when dealing with multiple tasks. This study aimed to combine functional near-infrared spectroscopy (fNIRS) and electrocardiogram (ECG) to detect changes in mental workload during multitasking in a simulated flight.

METHODS

Twenty-six participants performed three multitasking tasks at different mental workload levels. These mental workload levels were set by varying the number of subtasks. fNIRS and ECG signals were recorded during tasks. Participants filled in the national aeronautics and space administration task load index (NASA-TLX) scale after each task. The effects of mental workload on scores of NASA-TLX, performance of tasks, heart rate (HR), heart rate variability (HRV), and the prefrontal cortex (PFC) activation were analyzed.

RESULTS

Compared to multitasking in lower mental workload conditions, participants exhibited higher scores of NASA-TLX, HR, and PFC activation when multitasking in high mental workload conditions. Their performance was worse during the high mental workload multitasking condition, as evidenced by the higher average tracking distance, smaller number of response times, and longer response time of the meter. The standard deviation of the RR intervals (SDNN) was negatively correlated with subjective mental workload in the low task load condition and PFC activation was positively correlated with HR and subjective mental workload in the medium task load condition.

CONCLUSION

HR and PFC activation can be used to detect changes in mental workload during simulated flight multitasking tasks.

Neuroadaptive Training via fNIRS in Flight Simulators

Training to master a new skill often takes a lot of time, effort, and financial resources, particularly when the desired skill is complex, time sensitive, or high pressure where lives may be at risk. Professions such as aircraft pilots, surgeons, and other mission-critical operators that fall under this umbrella require extensive domain-specific dedicated training to enable learners to meet real-world demands. In this study, we describe a novel neuroadaptive training protocol to enhance learning speed and efficiency using a neuroimaging-based cognitive workload measurement system in a flight simulator. We used functional near-infrared spectroscopy (fNIRS), which is a wearable, mobile, non-invasive neuroimaging modality that can capture localized hemodynamic response and has been used extensively to monitor the anterior prefrontal cortex to estimate cognitive workload. The training protocol included four sessions over 2 weeks and utilized realistic piloting tasks with up to nine levels of difficulty. Learners started at the lowest level and their progress adapted based on either behavioral performance and fNIRS measures combined (neuroadaptive) or performance measures alone (control). Participants in the neuroadaptive group were found to have significantly more efficient training, reaching higher levels of difficulty or significantly improved performance depending on the task, and showing consistent patterns of hemodynamic-derived workload in the dorsolateral prefrontal cortex. The results of this study suggest that a neuroadaptive personalized training protocol using non-invasive neuroimaging is able to enhance learning of new tasks. Finally, we outline here potential avenues for further optimization of this fNIRS based neuroadaptive training approach. As fNIRS mobile neuroimaging is becoming more practical and accessible, the approaches developed here can be applied in the real world in scale.

Investigating Differences in Behavior and Brain in Human-Human and Human-Autonomous Vehicle Interactions in Time-Critical Situations

Some studies provide evidence that humans could actively exploit the alleged technological advantages of autonomous vehicles (AVs). This implies that humans may tend to interact differently with AVs as compared to human driven vehicles (HVs) with the knowledge that AVs are programmed to be risk-averse. Hence, it is important to investigate how humans interact with AVs in complex traffic situations. Here, we investigated whether participants would value interactions with AVs differently compared to HVs, and if these differences can be characterized on the behavioral and brain-level. We presented participants with a cover story while recording whole-head brain activity using fNIRS that they were driving under time pressure through urban traffic in the presence of other HVs and AVs. Moreover, the AVs were programmed defensively to avoid collisions and had faster braking reaction times than HVs. Participants would receive a monetary reward if they managed to finish the driving block within a given time-limit without risky driving maneuvers. During the drive, participants were repeatedly confronted with left-lane turning situations at unsignalized intersections. They had to stop and find a gap to turn in front of an oncoming stream of vehicles consisting of HVs and AVs. While the behavioral results did not show any significant difference between the safety margin used during the turning maneuvers with respect to AVs or HVs, participants tended to be more certain in their decision-making process while turning in front of AVs as reflected by the smaller variance in the gap size acceptance as compared to HVs. Importantly, using a multivariate logistic regression approach, we were able to predict whether the participants decided to turn in front of HVs or AVs from whole-head fNIRS in the decision-making phase for every participant (mean accuracy = 67.2%, SD = 5%). Channel-wise univariate fNIRS analysis revealed increased brain activation differences for turning in front of AVs compared to HVs in brain areas that represent the valuation of actions taken during decision-making. The insights provided here may be useful for the development of control systems to assess interactions in future mixed traffic environments involving AVs and HVs.

Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications

Background

Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application.

Methods

The signal acquisition configuration was optimized by analyzing the feature importance in mental workload recognition model and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical Multi-Task Attribute Battery (MATB) task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected.

Results

A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 76.25 ± 5.21%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O₂Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta_{1}$$\end{document}β1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta_{2}$$\end{document}β2 bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O₂Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks.

Conclusions

The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 76.25 ± 5.21% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems.

Functional Connectivity Within the Fronto-Parietal Network Predicts Complex Task Performance: A fNIRS Study

Performance in complex tasks is essential for many high risk operators. The achievement of such tasks is supported by high-level cognitive functions arguably involving functional activity and connectivity in a large ensemble of brain areas that form the fronto-parietal network. Here we aimed at determining whether the functional connectivity at rest within this network could predict performance in a complex task: the Space Fortress video game. Functional Near Infrared Spectroscopy (fNIRS) data from 32 participants were recorded during a Resting-State period, the completion of a simple version of Space Fortress (monotask) and the original version (multitask). The intrinsic functional connectivity within the fronto-parietal network (i.e., during the Resting-State) was a significant predictor of performance at Space Fortress multitask but not at its monotask version. The same pattern was observed for the functional connectivity during the task. Our overall results suggest that Resting-State functional connectivity within the fronto-parietal network could be used as an intrinsic brain marker for performance prediction of a complex task achievement, but not for simple task performance.

Leading indicators of mental representation in construction hazard recognition

Hazard recognition is mainly a visual search and cognitive process. Mental representations of hazards may impact mental states of hazard recognition. We assessed the effects of critical indicators of mental presentations of construction hazards on prefrontal cortex activation, a proxy for the mental states of hazard recognition. Students participated in a hazard inspection experiment, with near-infrared spectroscopy (NIRS) used to record prefrontal cortex activation. The effects of critical indicators of the hazards' mental representations on prefrontal activation were analyzed. Results demonstrated that site familiarity, risk tolerance and safety knowledge have significant effects on medial prefrontal activation for hazards at a low visual clutter level. High levels of site familiarity and risk tolerance reduced medial prefrontal activation and saved cognitive resources. Theoretically, the findings supplement the knowledge of safety hazards' mental representations; and practically, the findings guide provision of individual-specific guidance for improving workers' hazard inspection performance.

Identifying Interactive Factors That May Increase Crash Risk between Young Drivers and Trucks: A Narrative Review

Sharing the road with trucks is associated with increased risk of serious injury and death for passenger vehicle drivers. However, the onus for minimising risk lies not just with truck drivers; other drivers must understand the unique performance limitations of trucks associated with stopping distances, blind spots, and turning manoeuverability, so they can suitably act and react around trucks. Given the paucity of research aimed at understanding the specific crash risk vulnerability of young drivers around trucks, the authors employ a narrative review methodology that brings together evidence from both truck and young driver road safety research domains, as well as data regarding known crash risks for each driving cohort, to gain a comprehensive understanding of what young drivers are likely to know about heavy vehicle performance limitations, where there may be gaps in their understanding, and how this could potentially increase crash risk. We then review literature regarding the human factors affecting young drivers to understand how perceptual immaturity and engagement in risky driving behaviours are likely to compound risk regarding both the frequency and severity of collision between trucks and young drivers. Finally, we review current targeted educational initiatives and suggest that simply raising awareness of truck limitations is insufficient. We propose that further research is needed to ensure initiatives aimed at increasing young driver awareness of trucks and truck safety are evidence-based, undergo rigorous evaluation, and are delivered in a way that aims to (i) increase young driver risk perception skills, and (ii) reduce risky driving behaviour around trucks.

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 various types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. At the macro scale, bibliometric aspects of these studies are analysed. At the micro scale, 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 neural activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Potential topics in driving behaviour research are identified that could benefit from the adoption of neuroimaging methods in future studies. In terms of practicality, while fMRI and MEG experiments have proven rather invasive and technologically challenging for adoption in driving behaviour research, EEG and fNIRS applications have been more diverse. They have even been tested beyond simulated driving settings, in field driving experiments. Advantages and limitations of each of these four neuroimaging methods in the context of driving behaviour experiments are outlined in the paper.

A Methodological Review of fNIRS in Driving Research: Relevance to the Future of Autonomous Vehicles

As automobile manufacturers have begun to design, engineer, and test autonomous driving systems of the future, brain imaging with functional near-infrared spectroscopy (fNIRS) can provide unique insights about cognitive processes associated with evolving levels of autonomy implemented in the automobile. Modern fNIRS devices provide a portable, relatively affordable, and robust form of functional neuroimaging that allows researchers to investigate brain function in real-world environments. The trend toward "naturalistic neuroscience" is evident in the growing number of studies that leverage the methodological flexibility of fNIRS, and in doing so, significantly expand the scope of cognitive function that is accessible to observation via functional brain imaging (i.e., from the simulator to on-road scenarios). While more than a decade's worth of study in this field of fNIRS driving research has led to many interesting findings, the number of studies applying fNIRS during autonomous modes of operation is limited. To support future research that directly addresses this lack in autonomous driving research with fNIRS, we argue that a cogent distillation of the methods used to date will help facilitate and streamline this research of tomorrow. To that end, here we provide a methodological review of the existing fNIRS driving research, with the overarching goal of highlighting the current diversity in methodological approaches. We argue that standardization of these approaches will facilitate greater overlap of methods by researchers from all disciplines, which will, in-turn, allow for meta-analysis of future results. We conclude by providing recommendations for advancing the use of such fNIRS technology in furthering understanding the adoption of safe autonomous vehicle technology.

Cerebral haemodynamics during simulated driving: Changes in workload are detectable with functional near infrared spectroscopy

Motor vehicle operation is a complicated task and substantial cognitive resources are required for safe driving. Experimental paradigms examining cognitive workload using driving simulators often introduce secondary tasks, such as mathematical exercises, or utilise simulated in-vehicle information systems. The effects of manipulating the demands of the primary driving task have not been examined in detail using advanced neuroimaging techniques. This study used a manipulation of the simulated driving environment to test the impact of increased driving complexity on brain activity. Fifteen participants drove in two scenarios reflecting common driving environments differing in the amount of vehicular traffic, frequency of intersections, number of buildings, and speed limit restrictions. Functional near infrared spectroscopy was used to quantify changes in cortical activity; fifty-five optodes were placed over the prefrontal and occipital cortices, commonly assessed areas during driving. Compared to baseline, both scenarios increased oxyhaemoglobin in the bilateral prefrontal cortex and cerebral blood volume in the right prefrontal cortex (all p ≤ 0.05). Deoxyhaemoglobin decreased at the bilateral aspects of the prefrontal cortex but overall tended to increase in the medial aspect during both scenarios (both p ≤ 0.05). Cerebral oxygen exchange significantly declined at the lateral aspects of the prefrontal cortex, with a small but significant increase seen in the medial aspect (both p < 0.05). There were no significant differences for oxyhaemoglobin, deoxyhaemoglobin, or cerebral blood volume (all p > 0.05). This study demonstrates that functional near infrared spectroscopy is capable of detecting changes in cortical activity elicited by simulated driving tasks but may be less sensitive to variations in driving workload aggregated over a longer duration.

The Role of the Prefrontal Cortex and Functional Connectivity during Maritime Operations: An fNIRS study

INTRODUCTION

Watchkeeping is a significant activity during maritime operations, and failures of sustained attention and decision-making can increase the likelihood of a collision.

METHODS

A study was conducted in a ship bridge simulator where 40 participants (20 experienced/20 inexperienced) performed: (1) a 20-min period of sustained attention to locate a target vessel and (2) a 10-min period of decision-making/action selection to perform an evasive maneuver. Half of the participants also performed an additional task of verbally reporting the position of their vessel. Activation of the prefrontal cortex (PFC) was captured via a 15-channel functional near-infrared spectroscopy (fNIRS) montage, and measures of functional connectivity were calculated frontal using graph-theoretic measures.

RESULTS

Neurovascular activation of right lateral area of the PFC decreased during sustained attention and increased during decision-making. The graph-theoretic analysis revealed that density declined during decision-making in comparison with the previous period of sustained attention, while local clustering declined during sustained attention and increased when participants prepared their evasive maneuver. A regression analysis revealed an association between network measures and behavioral outcomes, with respect to spotting the target vessel and making an evasive maneuver.

CONCLUSIONS

The right lateral area of the PFC is sensitive to watchkeeping and decision-making during operational performance. Graph-theoretic measures allow us to quantify patterns of functional connectivity and were predictive of safety-critical performance.

Methodological Approaches and Recommendations for Functional Near-Infrared Spectroscopy Applications in HF/E Research

OBJECTIVE

The objective of this study was to systematically document current methods and protocols employed when using functional near-infrared spectroscopy (fNIRS) techniques in human factors and ergonomics (HF/E) research and generate recommendations for conducting and reporting fNIRS findings in HF/E applications.

METHOD

A total of 1,687 articles were identified through Ovid-MEDLINE, PubMed, Web of Science, and Scopus databases, of which 37 articles were included in the review based on review inclusion/exclusion criteria.

RESULTS

A majority of the HF/E fNIRS investigations were found in transportation, both ground and aviation, and in assessing cognitive (e.g., workload, working memory) over physical constructs. There were large variations pertaining to data cleaning, processing, and analysis approaches across the studies that warrant standardization of methodological approaches. The review identified major challenges in transparency and reporting of important fNIRS data collection and analyses specifications that diminishes study replicability, introduces potential biases, and increases likelihood of inaccurate results. As such, results reported in existing fNIRS studies need to be cautiously approached.

CONCLUSION

To improve the quality of fNIRS investigations and/or to facilitate its adoption and integration in different HF/E applications, such as occupational ergonomics and rehabilitation, recommendations for fNIRS data collection, processing, analysis, and reporting are provided.

Road user hazard perception tests: A systematic review of current methodologies

BACKGROUND

Poor hazard perception, or the ability to anticipate potentially dangerous road and traffic situations, has been linked to an increased crash risk. Novice and younger road users are typically poorer at hazard perception than experienced and older road users. Road traffic authorities have recognised the importance of hazard perception skills, with the inclusion of a hazard perception test in most Graduated Driver Licensing (GDL) systems.

OBJECTIVES

This review synthesises studies of hazard perception tests in order to determine best practice methodologies that discriminate between novice/younger and experienced/older road users.

DATA SOURCES

Published studies available on PsychInfo, Scopus and Medline as at April 2018 were included in the review. Studies included a hazard perception test methodology and compared non-clinical populations of road users (car drivers, motorcyclists, bicyclists and pedestrians), based on age and experience, or compared methodologies.

RESULTS

49 studies met the inclusion criteria. There was a high degree of heterogeneity in the studies. However all methodologies - video, static image, simulator and real-world test-drive were able to discriminate road user groups categorised by age and/or experience, on at least one measure of hazard perception.

CONCLUSIONS

Whilst there was a high level of heterogeneity of studies, video methodology utilising temporal responses (e.g. press a button when detecting the potential hazard) are a consistent measure of hazard perception across road user groups, whereas spatial measures (e.g. locate potential hazard in the scenario) were inconsistent. Staged footage was found to discriminate as well as unstaged footage, with static images also adding valuable information on hazard perception. There were considerable inconsistencies in the categorising of participants based on age and experience, limited application of theoretical frameworks, and a considerable lack of detail regarding post hoc amendments of hazardous scenarios. This research can guide further developments in hazard perception testing that may improve driver licensing and outcomes for road users.

Influences of age, mental workload, and flight experience on cognitive performance and prefrontal activity in private pilots: a fNIRS study

The effects of aging on cognitive performance must be better understood, especially to protect older individuals who are engaged in risky activities (e.g. aviation). Current literature on executive functions suggests that brain compensatory mechanisms may counter cognitive deterioration due to aging, at least up to certain task load levels. The present study assesses this hypothesis in private pilots engaged in two executive tasks from the standardized CANTAB battery, namely Spatial Working Memory (SWM) and  One Touch Stockings of Cambridge (OTS). Sixty-one pilots from three age groups (young, middle-aged, older) performed these two tasks from low to very high difficulty levels, beyond those reported in previous aging studies. A fNIRS headband measured changes in oxyhemoglobin (HbO2) in the prefrontal cortex. Results confirmed an overall effect of the difficulty level in the three age groups, with a decline in task performance and an increase in prefrontal HbO2 signal. Performance of older relative to younger pilots was impaired in both tasks, with the greatest impairment observed for the highest-load Spatial Working Memory task. Consistent with this behavioral deficit in older pilots, a plateau of prefrontal activity was observed at this highest-load level, suggesting that a ceiling in neural resources was reached. When behavioral performance was either equivalent between age groups or only slightly impaired in the older group, there were not any age-related differences in prefrontal activity. Finally, older pilots with extensive flying experience tend to show better preserved spatial working memory performance when compared to mildly-experienced of the same age group. The present findings are discussed in the frames of HAROLD and CRUNCH theoretical models of cognitive and neural aging, evoking the possibility that piloting expertise may contribute to preserve executive functions throughout adulthood.

Demonstrating Brain-Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near-Infrared Spectroscopy

Driving is a complex task concurrently drawing on multiple cognitive resources. Yet, there is a lack of studies investigating interactions at the brain-level among different driving subtasks in dual-tasking. This study investigates how visuospatial attentional demands related to increased driving difficulty interacts with different working memory load (WML) levels at the brain level. Using multichannel whole-head high density functional near-infrared spectroscopy (fNIRS) brain activation measurements, we aimed to predict driving difficulty level, both separate for each WML level and with a combined model. Participants drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. In half of the time, the course led through a construction site with reduced lane width, increasing visuospatial attentional demands. Concurrently, participants performed a modified version of the n-back task with five different WML levels (from 0-back up to 4-back), forcing them to continuously update, memorize, and recall the sequence of the previous 'n' speed signs and adjust their speed accordingly. Using multivariate logistic ridge regression, we were able to correctly predict driving difficulty in 75.0% of the signal samples (1.955 Hz sampling rate) across 15 participants in an out-of-sample cross-validation of classifiers trained on fNIRS data separately for each WML level. There was a significant effect of the WML level on the driving difficulty prediction accuracies [range 62.2-87.1%; χ2(4) = 19.9, p < 0.001, Kruskal-Wallis H test] with highest prediction rates at intermediate WML levels. On the contrary, training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46.8%). Activation changes in the bilateral dorsal frontal (putative BA46), bilateral inferior parietal (putative BA39), and left superior parietal (putative BA7) areas were most predictive to increased driving difficulty. These discriminative patterns diminished at higher WML levels indicating that visuospatial attentional demands and WML involve interacting underlying brain processes. The changing pattern of driving difficulty related brain areas across WML levels could indicate potential changes in the multitasking strategy with level of WML demand, in line with the multiple resource theory.

Mental workload is reflected in driver behaviour, physiology, eye movements and prefrontal cortex activation

Mental workload is an important factor during driving, as both high and low levels may result in driver error. This research examined the mental workload of drivers caused by changes in road environment and how such changes impact upon behaviour, physiological responses, eye movements and brain activity. The experiment used functional near infrared spectroscopy to record prefrontal cortex activation associated with changes in mental workload during simulated driving. Increases in subjective ratings of mental workload caused by changes in road type were accompanied by increases in skin conductance, acceleration signatures and horizontal spread of search. Such changes were also associated with increases in the concentration of oxygenated haemoglobin in the prefrontal cortex. Mental workload fluctuates during driving. Such changes can be identified using a range of measures which could be used to inform the development of in-vehicle devices and partially autonomous systems.

Impact of gender, organized athletics, and video gaming on driving skills in novice drivers

Given that novice drivers tend to be young, and teenagers and young adult drivers are involved in the greatest number of accidents, it is important that we understand what factors impact the driving skills of this population of drivers. The primary aim of the present study was to understand the impact of gender, organized athletics, and video gaming on driving skills of novice drivers under real-world driving conditions. Novice driving students having less than five hours driving experience previous to a normal driving lesson were evaluated on their self-confidence (self-reported) prior to the lesson and driving skill evaluated by their instructor during the course of the lesson. Information was collected about gender, age, whether or not the students were involved in organized athletics, and the extent of their video game playing. There was no impact of gender or extent of video game playing on driving skills. Females were significantly less self-confident with driving than males, but this did not translate to gender differences in driving skills. Being involved in organized athletics-either currently or in the past-significantly enhanced driving skills in both females and males. Finally, novice drivers' age was negatively correlated with driving skills. That is, younger novice drivers (especially males) had better driving skills than older novice drivers. This is counter to popular belief that young drivers lack technical driving skills because they have less experience behind the wheel. Based on the results of the current study, we hypothesize that the relatively high accident rate of younger drivers (especially male drivers) is most likely due to inattention to safety considerations rather than lack of technical driving ability.

Detecting Pilot's Engagement Using fNIRS Connectivity Features in an Automated vs. Manual Landing Scenario

Monitoring pilot's mental states is a relevant approach to mitigate human error and enhance human machine interaction. A promising brain imaging technique to perform such a continuous measure of human mental state under ecological settings is Functional Near-InfraRed Spectroscopy (fNIRS). However, to our knowledge no study has yet assessed the potential of fNIRS connectivity metrics as long as passive Brain Computer Interfaces (BCI) are concerned. Therefore, we designed an experimental scenario in a realistic simulator in which 12 pilots had to perform landings under two contrasted levels of engagement (manual vs. automated). The collected data were used to benchmark the performance of classical oxygenation features (i.e., Average, Peak, Variance, Skewness, Kurtosis, Area Under the Curve, and Slope) and connectivity features (i.e., Covariance, Pearson's, and Spearman's Correlation, Spectral Coherence, and Wavelet Coherence) to discriminate these two landing conditions. Classification performance was obtained by using a shrinkage Linear Discriminant Analysis (sLDA) and a stratified cross validation using each feature alone or by combining them. Our findings disclosed that the connectivity features performed significantly better than the classical concentration metrics with a higher accuracy for the wavelet coherence (average: 65.3/59.9 %, min: 45.3/45.0, max: 80.5/74.7 computed for HbO/HbR signals respectively). A maximum classification performance was obtained by combining the area under the curve with the wavelet coherence (average: 66.9/61.6 %, min: 57.3/44.8, max: 80.0/81.3 computed for HbO/HbR signals respectively). In a general manner all connectivity measures allowed an efficient classification when computed over HbO signals. Those promising results provide methodological cues for further implementation of fNIRS-based passive BCIs.

Executive Function Capacities, Negative Driving Behavior and Crashes in Young Drivers

Motor vehicle crashes remain a leading cause of injury and death in adolescents, with teen drivers three times more likely to be in a fatal crash when compared to adults. One potential contributing risk factor is the ongoing development of executive functioning with maturation of the frontal lobe through adolescence and into early adulthood. Atypical development resulting in poor or impaired executive functioning (as in Attention-Deficit/Hyperactivity Disorder) has been associated with risky driving and crash outcomes. However, executive function broadly encompasses a number of capacities and domains (e.g., working memory, inhibition, set-shifting). In this review, we examine the role of various executive function sub-processes in adolescent driver behavior and crash rates. We summarize the state of methods for measuring executive control and driving outcomes and highlight the great heterogeneity in tools with seemingly contradictory findings. Lastly, we offer some suggestions for improved methods and practical ways to compensate for the effects of poor executive function (such as in-vehicle assisted driving devices). Given the key role that executive function plays in safe driving, this review points to an urgent need for systematic research to inform development of more effective training and interventions for safe driving among adolescents.

Utilizing functional near-infrared spectroscopy for prediction of cognitive workload in noisy work environments

Occupational noise frequently occurs in the work environment in military intelligence, surveillance, and reconnaissance operations. This impacts cognitive performance by acting as a stressor, potentially interfering with the analysts' decision-making process. We investigated the effects of different noise stimuli on analysts' performance and workload in anomaly detection by simulating a noisy work environment. We utilized functional near-infrared spectroscopy (fNIRS) to quantify oxy-hemoglobin (HbO) and deoxy-hemoglobin concentration changes in the prefrontal cortex (PFC), as well as behavioral measures, which include eye tracking, reaction time, and accuracy rate. We hypothesized that noisy environments would have a negative effect on the participant in terms of anomaly detection performance due to the increase in workload, which would be reflected by an increase in PFC activity. We found that HbO for some of the channels analyzed were significantly different across noise types ([Formula: see text]). Our results also indicated that HbO activation for short-intermittent noise stimuli was greater in the PFC compared to long-intermittent noises. These approaches using fNIRS in conjunction with an understanding of the impact on human analysts in anomaly detection could potentially lead to better performance by optimizing work environments.

Application of Functional Near-Infrared Spectroscopy to the Study of Brain Function in Humans and Animal Models

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique that indirectly assesses neuronal activity by measuring changes in oxygenated and deoxygenated hemoglobin in tissues using near-infrared light. fNIRS has been used not only to investigate cortical activity in healthy human subjects and animals but also to reveal abnormalities in brain function in patients suffering from neurological and psychiatric disorders and in animals that exhibit disease conditions. Because of its safety, quietness, resistance to motion artifacts, and portability, fNIRS has become a tool to complement conventional imaging techniques in measuring hemodynamic responses while a subject performs diverse cognitive and behavioral tasks in test settings that are more ecologically relevant and involve social interaction. In this review, we introduce the basic principles of fNIRS and discuss the application of this technique in human and animal studies.

Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS

An improved understanding of how the brain allocates mental resources as a function of task difficulty is critical for enhancing human performance. Functional near infrared spectroscopy (fNIRS) is a field-deployable optical brain monitoring technology that provides a direct measure of cerebral blood flow in response to cognitive activity. We found that fNIRS was sensitive to variations in task difficulty in both real-life (flight simulator) and laboratory settings (tests measuring executive functions), showing increased concentration of oxygenated hemoglobin (HbO2) and decreased concentration of deoxygenated hemoglobin (HHb) in the prefrontal cortex as the tasks became more complex. Intensity of prefrontal activation (HbO2 concentration) was not clearly correlated to task performance. Rather, activation intensity shed insight on the level of mental effort, i.e., how hard an individual was working to accomplish a task. When combined with performance, fNIRS provided an estimate of the participants' neural efficiency, and this efficiency was consistent across levels of difficulty of the same task. Overall, our data support the suitability of fNIRS to assess the mental effort related to human operations and represents a promising tool for the measurement of neural efficiency in other contexts such as training programs or the clinical setting.

Assessing the Driver's Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study

Cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. We envision that driver assistive systems which adapt their functionality to the driver's cognitive state could be a promising approach to reduce road accidents due to human errors. This research attempts to predict variations of cognitive working memory load levels in a natural driving scenario with multiple parallel tasks and to reveal predictive brain areas. We used a modified version of the n-back task to induce five different working memory load levels (from 0-back up to 4-back) forcing the participants to continuously update, memorize, and recall the previous 'n' speed sequences and adjust their speed accordingly while they drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. We measured brain activation using multichannel whole head, high density functional near-infrared spectroscopy (fNIRS) and predicted working memory load level from the fNIRS data by combining multivariate lasso regression and cross-validation. This allowed us to predict variations in working memory load in a continuous time-resolved manner with mean Pearson correlations between induced and predicted working memory load over 15 participants of 0.61 [standard error (SE) 0.04] and a maximum of 0.8. Restricting the analysis to prefrontal sensors placed over the forehead reduced the mean correlation to 0.38 (SE 0.04), indicating additional information gained through whole head coverage. Moreover, working memory load predictions derived from peripheral heart rate parameters achieved much lower correlations (mean 0.21, SE 0.1). Importantly, whole head fNIRS sampling revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital brain areas with increasing working memory load levels suggesting that these areas are specifically involved in workload-related processing.