Driving performance and EEG fluctuations during on-the-road driving following sleep deprivation

Mind Wandering during Implicit Learning Is Associated with Increased Periodic EEG Activity and Improved Extraction of Hidden Probabilistic Patterns

Mind wandering, occupying 30-50% of our waking time, remains an enigmatic phenomenon in cognitive neuroscience. A large number of studies showed a negative association between mind wandering and attention-demanding (model-based) tasks in both natural settings and laboratory conditions. Mind wandering, however, does not seem to be detrimental for all cognitive domains and was observed to benefit creativity and problem-solving. We examined if mind wandering may facilitate model-free processes, such as probabilistic learning, which relies on the automatic acquisition of statistical regularities with minimal attentional demands. We administered a well-established implicit probabilistic learning task combined with thought probes in healthy adults (N = 37, 30 females). To explore the neural correlates of mind wandering and probabilistic learning, participants were fitted with high-density electroencephalography. Our findings indicate that probabilistic learning was not only immune to periods of mind wandering but was positively associated with it. Spontaneous, as opposed to deliberate mind wandering, was particularly beneficial for extracting the probabilistic patterns hidden in the visual stream. Cortical oscillatory activity in the low-frequency (slow and delta) range, indicative of covert sleep-like states, was associated with both mind wandering and improved probabilistic learning, particularly in the early stages of the task. Given the importance of probabilistic implicit learning in predictive processing, our findings provide novel insights into the potential cognitive benefits of task-unrelated thoughts in addition to shedding light on its neural mechanisms.

Sensitivity of driving simulation to sleep deprivation: effect of task duration

STUDY OBJECTIVES

The Psychomotor Vigilance Task (PVT) is widely recognized as the gold standard for measuring vigilance, providing a rapid and objective measure of this state. While driving simulations are also used, they typically require longer administration times. This study examines the sensitivity of driving simulation variables to sleep deprivation throughout the task. The aim is to determine the shorter duration at which performance declines can be observed. A secondary goal is to compare driving simulation and PVT variables' sensitivity in detecting sleep deprivation.

METHODS

Forty-three participants (22 males; aged 46.7 ± 17.8 years) completed a 90-minute driving simulation and a 10-minute PVT under two conditions (normal sleep and partial sleep deprivation of 3.5 hours). Signed-rank Wilcoxon tests and effect sizes were computed for variables from both tasks. Effect sizes were calculated for each 10-minute interval to assess sensitivity over time.

RESULTS

All the variables showed sensitivity to sleep deprivation. The largest effect sizes were observed in the driving simulation and specifically for the standard deviation of lateral position (SDLP) (r = 0.73) and the standard deviation of steering wheel movement (r = 0.73). A large effect size for the SDLP (r = 0.71) was observed after only 20 minutes of driving. For the 10-minute PVT, the highest effect size was observed for the number of lapses (r = 0.52).

CONCLUSION

Driving-related variables are highly sensitive to sleep deprivation while providing continuous performance measurements. The SDLP is a particularly sensitive variable even with a reduced driving time of 20 minutes, suggesting that driving simulation tasks can be effectively shortened to 20 minutes.

Approaches to inducing mental fatigue: A systematic review and meta-analysis of (neuro)physiologic indices

Mental fatigue is a transient psychophysiological state characterized by impaired cognition and behavior across a range of dynamic contexts. Despite increasing interest in this phenomenon, its (neuro)physiologic representations remain unclear. This systematic review aimed to quantify the range of (neuro)physiologic outcomes and methodologies used to investigate mental fatigue in laboratory-based settings. Across the 72 studies meeting our inclusion criteria, we identified 30 unique physiologic, four visual outcomes, and the application of several neuroimaging techniques investigating neuronal function. Mental fatigue increased heart rate, systolic and diastolic blood pressure, mean arterial pressure, low frequency, and root mean square of successive differences (RMSSD), and reduced standard deviation of normal-to-normal intervals (SDNN) (all P ≤ 0.04) when compared with controls. Applying electroencephalography to investigate delta, theta, and alpha bandwidths may provide useful insights into this phenomenon, and functional near-infra-red spectroscopy to right-lateralized frontoparietal regions would be helpful to investigate cortical activity change in response to mental fatigue. More data are needed across a range of methodological contexts in order to further determine the (neuro)physiological manifestations of mental fatigue. However, this review provides direction to researchers and will assist them in navigating and considering the range of options available.

Research on mental fatigue during long-term motor imagery: a pilot study

Mental fatigue during long-term motor imagery (MI) may affect intention recognition in MI applications. However, the current research lacks the monitoring of mental fatigue during MI and the definition of robust biomarkers. The present study aims to reveal the effects of mental fatigue on motor imagery recognition at the brain region level and explore biomarkers of mental fatigue. To achieve this, we recruited 10 healthy participants and asked them to complete a long-term motor imagery task involving both right- and left-handed movements. During the experiment, we recorded 32-channel EEG data and carried out a fatigue questionnaire for each participant. As a result, we found that mental fatigue significantly decreased the subjects' motor imagery recognition rate during MI. Additionally the theta power of frontal, central, parietal, and occipital clusters significantly increased after the presence of mental fatigue. Furthermore, the phase synchronization between the central cluster and the frontal and occipital lobes was significantly weakened. To summarize, the theta bands of frontal, central, and parieto-occipital clusters may serve as powerful biomarkers for monitoring mental fatigue during motor imagery. Additionally, changes in functional connectivity between the central cluster and the prefrontal and occipital lobes during motor imagery could be investigated as potential biomarkers.

Effects of sleep deprivation and hazard types on the hazard perception of young novice drivers: An ERP study

OBJECTIVE

The present study aimed to explore the effects of sleep deprivation on young novice drivers' cognitive neural processing of different hazard types.

METHOD

A 2 (sleep deprivation group, control group) × 3 (no hazard, covert hazard, overt hazard) mixed experimental design was used. Twenty-eight young drivers were sleep-deprived (no sleep within the past 24 h), while 28 drivers were in the control group (maintaining a normal schedule throughout the week). Eighty pictures containing a covert hazard (20 pictures), overt hazard (20 pictures) and no hazard (40 pictures) were presented. Participants were asked to press the keyboard quickly if they detected a hazard situation. The reaction time, accuracy, and changes in the N1 (100-150 ms) and N2 (250-350 ms) components of event-related potentials (ERP) measured using electroencephalography (EEG) were obtained.

RESULTS

Compared to the control group, the response accuracy of sleep-deprived drivers was higher in the cover-hazard situation and their N1 latency was longer in the no-hazard situation. Compared to the no-hazard and overt-hazard situations, the participants' reaction times and N2 amplitudes were significantly greater, and the response accuracy was significantly lower in the covert-hazard situation.

CONCLUSION

Hazard perception is compromised when drivers are sleep-deprived, especially when they are confronted with covert hazard situations. The findings help understand the negative effects of sleep deprivation in the early stage of young novice drivers' hazard perception.

Sleep Architecture and Sleep EEG Alterations are Associated with Impaired Cognition Under Sleep Restriction

Purpose

Many studies have investigated the cognitive, emotional, and other impairments caused by sleep restriction. However, few studies have explored the relationship between cognitive performance and changes in sleep structure and electroencephalography (EEG) during sleep. The present study aimed to examine whether changes in sleep structure and EEG can account for cognitive impairment caused by sleep restriction.

Patients and Methods

Sixteen young adults spent five consecutive nights (adaptation 9h, baseline 8h, 1st restriction 6h, 2nd restriction 6h, and recovery 10h) in a sleep laboratory, with polysomnography recordings taken during sleep. Throughout waking periods in each condition, participants completed the psychomotor vigilance test (PVT), which measures vigilant attention, and the Go/No-Go task, which measures inhibition control.

Results

The results showed that sleep restriction significantly decreased the proportion of N1 and N2 sleep, increased the proportion of N3 sleep, and reduced the time spent awake after sleep onset (WASO) and sleep onset latency. Poorer performance on the PVT and Go/No Go task was associated with longer WASO, a larger proportion of N3 sleep, and a smaller proportion of N2 sleep. Additionally, the power spectral density of delta waves significantly increased after sleep restriction, and this increase predicted a decrease in vigilance and inhibition control the next day.

Conclusion

These findings suggest that sleep architecture and EEG signatures may partially explain cognitive impairment caused by sleep restriction.

Prediction of drowsiness using EEG signals in young Indonesian drivers

Indonesia is among the countries with the highest accident rates in the world. Fatigue and drowsiness are among the main causes of the increased risks of accidents in the road transport sector. Sleep-related factors (quality and quantity, time of day) and work-related factors significantly affect the development of fatigue. The EEG signal indicator is often referred to as the gold standard for measuring fatigue and drowsiness. However, previous studies focused primarily on the trends of EEG signals under certain conditions but overlooking the development of drowsiness indicators based on EEG signals. Furthermore, existing studies still do not agree on what parameters in the EEG signal indicator are best at detecting drowsiness. Thus, this study aims to design an EEG signal-based drowsiness indicator under simulated driving conditions. Drowsy drivers were monitored through EEG signal indicators and subjective assessments. The methods used in this study include statistical significance tests, logistic regression, and support vector machine. The results showed that sleep deprivation had a significant effect on increasing alpha, beta, and theta waves. In addition, driving duration significantly increased the theta power and all EEG ratios and decreased the beta power in the alert group. The ratio of (θ + α)/β and θ/β in the SD group also showed a considerable increase in the end of driving. Furthermore, sleep status and driving duration both influenced subjective sleepiness. EEG signals combined with sleep status and driving duration factors generated acceptable model accuracies (77.1% and 90.2% in training and testing, respectively), with 90.5% sensitivity and 90% specificity in data test. Support vector machine showed better classification than that of logistics regression, with the linear kernel as the best classifier. Theta power had the highest effect in the model compared with other EEG signals.

Understanding mental fatigue and its detection: a comparative analysis of assessments and tools

Mental fatigue has shown to be one of the root causes of decreased productivity and overall cognitive performance, by decreasing an individual's ability to inhibit responses, process information and concentrate. The effects of mental fatigue have led to occupational errors and motorway accidents. Early detection of mental fatigue can prevent the escalation of symptoms that may lead to chronic fatigue syndrome and other disorders. To date, in clinical settings, the assessment of mental fatigue and stress is done through self-reported questionnaires. The validity of these questionnaires is questionable, as they are highly subjective measurement tools and are not immune to response biases. This review examines the wider presence of mental fatigue in the general population and critically compares its various detection techniques (i.e., self-reporting questionnaires, heart rate variability, salivary cortisol levels, electroencephalogram, and saccadic eye movements). The ability of these detection tools to assess inhibition responses (which are sensitive enough to be manifested in a fatigue state) is specifically evaluated for a reliable marker in identifying mentally fatigued individuals. In laboratory settings, antisaccade tasks have been long used to assess inhibitory control and this technique can potentially serve as the most promising assessment tool to objectively detect mental fatigue. However, more studies need to be conducted in the future to validate and correlate this assessment with other existing measures of mental fatigue detection. This review is intended for, but not limited to, mental health professionals, digital health scientists, vision researchers, and behavioral scientists.

The measurement of mental fatigue following an overnight on-call duty among doctors using electroencephalogram

This study aimed to measure the spectral power differences in the brain rhythms among a group of hospital doctors before and after an overnight on-call duty. Thirty-two healthy doctors who performed regular on-call duty in a tertiary hospital in Sarawak, Malaysia were voluntarily recruited into this study. All participants were interviewed to collect relevant background information, followed by a self-administered questionnaire using Chalder Fatigue Scale and electroencephalogram test before and after an overnight on-call duty. The average overnight sleep duration during the on-call period was 2.2 hours (p<0.001, significantly shorter than usual sleep duration) among the participants. The mean (SD) Chalder Fatigue Scale score of the participants were 10.8 (5.3) before on-call and 18.4 (6.6) after on-call (p-value < 0.001). The theta rhythm showed significant increase in spectral power globally after an overnight on-call duty, especially when measured at eye closure. In contrast, the alpha and beta rhythms showed reduction in spectral power, significantly at temporal region, at eye closure, following an overnight on-call duty. These effects are more statistically significant when we derived the respective relative theta, alpha, and beta values. The finding of this study could be useful for development of electroencephalogram screening tool to detect mental fatigue.

Neuroergonomics on the Go: An Evaluation of the Potential of Mobile EEG for Workplace Assessment and Design

OBJECTIVE

We demonstrate and discuss the use of mobile electroencephalogram (EEG) for neuroergonomics. Both technical state of the art as well as measures and cognitive concepts are systematically addressed.

BACKGROUND

Modern work is increasingly characterized by information processing. Therefore, the examination of mental states, mental load, or cognitive processing during work is becoming increasingly important for ergonomics.

RESULTS

Mobile EEG allows to measure mental states and processes under real live conditions. It can be used for various research questions in cognitive neuroergonomics. Besides measures in the frequency domain that have a long tradition in the investigation of mental fatigue, task load, and task engagement, new approaches-like blink-evoked potentials-render event-related analyses of the EEG possible also during unrestricted behavior.

CONCLUSION

Mobile EEG has become a valuable tool for evaluating mental states and mental processes on a highly objective level during work. The main advantage of this technique is that working environments don't have to be changed while systematically measuring brain functions at work. Moreover, the workflow is unaffected by such neuroergonomic approaches.

How Tired is Too Tired to Drive? A Systematic Review Assessing the Use of Prior Sleep Duration to Detect Driving Impairment

Driver fatigue is a contributory factor in approximately 20% of vehicle crashes. While other causal factors (eg, drink-driving) have decreased in recent decades due to increased public education strategies and punitive measures, similar decreases have not been seen in fatigue-related crashes. Fatigued driving could be managed in a similar way to drink-driving, with an established point (ie, amount of prior sleep) after which drivers are "deemed impaired". This systematic review aimed to provide an evidence-base for the concept of deemed impairment and to identify how much prior sleep may be required to drive safely. Four online databases were searched (PubMed, Web of Science, Scopus, Embase). Eligibility requirements included a) measurement of prior sleep duration and b) driving performance indicators (eg, lane deviation) and/or outcomes (eg, crash likelihood). After screening 1940 unique records, a total of 61 studies were included. Included studies were categorised as having experimental/quasi-experimental (n = 21), naturalistic (n = 3), longitudinal (n = 1), case-control (n = 11), or cross-sectional (n = 25) designs. Findings suggest that after either 6 or 7 hours of prior sleep, a modest level of impairment is generally seen compared with after ≥ 8 hours of prior sleep (ie, well rested), depending on the test used. Crash likelihood appears to be ~30% greater after 6 or 7 hours of prior sleep, as compared to individuals who are well rested. After one night of either 4 or 5 hours of sleep, there are large decrements to driving performance and approximately double the likelihood of a crash when compared with well-rested individuals. When considering the scientific evidence, it appears that there is a notable decrease in driving performance (and associated increase in crash likelihood) when less than 5h prior sleep is obtained. This is a critical first step in establishing community standards regarding the amount of sleep required to drive safely.

An exploratory study of drivers' EEG response during emergent collision avoidance

INTRODUCTION

EEG (electroencephalogram) has been applied as a valuable measure to estimate drivers' mental status and cognitive workload during driving tasks. However, most previous studies have focused on the EEG features at particular driver status, such as fatigue or distraction, with less attention paid to EEG response in emergent and safety-critical situations. This study aims to investigate the underlying patterns of different EEG components during an emergent collision avoidance process.

METHOD

A driving simulator experiment was conducted with 38 participants (19 females and 19 males). The scenario included a roadside pedestrian who suddenly crossed the road when the driver approached. The participants' EEG data were collected during the pedestrian-collision avoidance process. The log-transformed power and power ratio of four typical EEG components (i.e., delta, theta, alpha and beta) were extracted from four collision avoidance stages: Stage 1-normal driving stage, Stage 2-hazard perception stage, Stage 3-evasive action stage, and Stage 4-post-hazard stage.

RESULTS

The activities of all four EEG bands changed consistently during the collision avoidance process, with the power increased significantly from Stage 1 to Stage 4. Drivers who collided with the pedestrian and drivers who avoided the collision successfully did not show a significant difference in EEG activity across the stages. Male drivers had a higher delta power ratio and lower alpha power ratio than females in both hazard perception and evasive action stages.

CONCLUSIONS

Enhanced activities of different EEG bands could be concurrent at emergent and safety-critical situations. Female drivers were more mentally aroused than male drivers during the collision avoidance process.

PRACTICAL APPLICATIONS

The study generates more understanding of drivers' neurophysiological response in an emergent and safety-critical collision avoidance event. Driver state monitoring and warning systems that aim to assist drivers in impending collisions may utilize the patterns of EEG activity identified in the collision avoidance process.

Effects of whole-body vibration on driver drowsiness: A review

INTRODUCTION

Whole-body vibration has direct impacts on driver vigilance by increasing physical and cognitive stress on the driver, which leads to drowsiness, fatigue and road traffic accidents. Although sleep deprivation, sleep apnoea and alcohol consumption can also lead to driver drowsiness, exposure to steady vibration is the factor most readily controlled by changes to vehicle design, yet it has received comparatively less attention.

METHODS

This review investigated interrelationships between the various components of whole-body vibration and the physiological and cognitive parameters that lead to driver drowsiness, as well as the effects of vibration parameters (frequency, amplitude, waveform and duration). Vibrations transmitted to the driver body from the vehicle floor and/or seat have been considered for this review, whereas hand-arm vibration, shocks, acute or transient vibration were excluded from consideration.

RESULTS

Drowsiness is affected by interactions between the frequency, amplitude, waveform and duration of the vibration. Under optimal conditions, whole-body vibration can induce significant drowsiness within 30 min. Low frequency whole-body vibrations, particularly vibrations of 4-10 Hz, are most effective at inducing drowsiness. This review notes some limitations of current studies and suggests directions for future research.

CONCLUSIONS

This review demonstrated a strong causal link exists between whole-body vibration and driver drowsiness. Since driver drowsiness has been established to be a significant contributor to motor vehicle accidents, research is needed to identify ways to minimise the components of whole-body vibration that contribute to drowsiness, as well as devising more effective ways to counteract drowsiness.

PRACTICAL APPLICATIONS

By raising awareness of the vibrational factors that contribute to drowsiness, manufacturers will be prompted to design vehicles that reduce the influence of these factors.

Let Complexity Bring Clarity: A Multidimensional Assessment of Cognitive Load Using Physiological Measures

The effects of cognitive load on driver behavior and traffic safety are unclear and in need of further investigation. Reliable measures of cognitive load for use in research and, subsequently, in the development and implementation of driver monitoring systems are therefore sought. Physiological measures are of interest since they can provide continuous recordings of driver state. Currently, however, a few issues related to their use in this context are not usually taken into consideration, despite being well-known. First, cognitive load is a multidimensional construct consisting of many mental responses (cognitive load components) to added task demand. Yet, researchers treat it as unidimensional. Second, cognitive load does not occur in isolation; rather, it is part of a complex response to task demands in a specific operational setting. Third, physiological measures typically correlate with more than one mental state, limiting the inferences that can be made from them individually. We suggest that acknowledging these issues and studying multiple mental responses using multiple physiological measures and independent variables will lead to greatly improved measurability of cognitive load. To demonstrate the potential of this approach, we used data from a driving simulator study in which a number of physiological measures (heart rate, heart rate variability, breathing rate, skin conductance, pupil diameter, eye blink rate, eye blink duration, EEG alpha power, and EEG theta power) were analyzed. Participants performed a cognitively loading n-back task at two levels of difficulty while driving through three different traffic scenarios, each repeated four times. Cognitive load components and other coinciding mental responses were assessed by considering response patterns of multiple physiological measures in relation to multiple independent variables. With this approach, the construct validity of cognitive load is improved, which is important for interpreting results accurately. Also, the use of multiple measures and independent variables makes the measurements (when analyzed jointly) more diagnostic-that is, better able to distinguish between different cognitive load components. This in turn improves the overall external validity. With more detailed, diagnostic, and valid measures of cognitive load, the effects of cognitive load on traffic safety can be better understood, and hence possibly mitigated.

Observing changes in human functioning during induced sleep deficiency and recovery periods

Prolonged periods of sleep restriction seem to be common in the contemporary world. Sleep loss causes perturbations of circadian rhythmicity and degradation of waking alertness as reflected in attention, cognitive efficiency and memory. Understanding whether and how the human brain recovers from chronic sleep loss is important not only from a scientific but also from a public health perspective. In this work we report on behavioral, motor, and neurophysiological correlates of sleep loss in healthy adults in an unprecedented study conducted in natural conditions and comprising 21 consecutive days divided into periods of 4 days of regular life (a baseline), 10 days of chronic partial sleep restriction (30% reduction relative to individual sleep need) and 7 days of recovery. Throughout the whole experiment we continuously measured the spontaneous locomotor activity by means of actigraphy with 1-minute resolution. On a daily basis the subjects were undergoing EEG measurements (64-electrodes with 500 Hz sampling frequency): resting state with eyes open and closed (8 minutes long each) followed by Stroop task lasting 22 minutes. Altogether we analyzed actigraphy (distributions of rest and activity durations), behavioral measures (reaction times and accuracy from Stroop task) and EEG (amplitudes, latencies and scalp maps of event-related potentials from Stroop task and power spectra from resting states). We observed unanimous deterioration in all the measures during sleep restriction. Further results indicate that a week of recovery subsequent to prolonged periods of sleep restriction is insufficient to recover fully. Only one measure (mean reaction time in Stroop task) reverted to baseline values, while the others did not.

I think I'm sleepy, therefore I am - Awareness of sleepiness while driving: A systematic review

Driver drowsiness contributes to 10-20% of motor vehicle crashes. To reduce crash risk, ideally drivers would be aware of the drowsy state and cease driving. The extent to which drivers can accurately identify sleepiness remains under much debate. We systematically examined whether individuals are aware of sleepiness while driving, and whether this accurately reflects driving impairment, using meta-analyses and narrative review. Within this scope, there is high variability in measures of subjective sleepiness, driving performance and physiologically-derived drowsiness, and statistical analyses. Thirty-four simulated/naturalistic driving studies were reviewed. To summarise, drivers were aware of sleepiness, and this was associated to physiological drowsiness and driving impairment, such that high levels of sleepiness significantly predicted crash events and lane deviations. Subjective sleepiness was more strongly correlated (i) with physiological drowsiness compared to driving outcomes; (ii) under simulated driving conditions compared to naturalistic drives; and (iii) when examined using the Karolinska sleepiness scale (KSS) compared to other measures. Gaps remain in relation to how age, sex, and varying degrees of sleep loss may influence this association. This review provides evidence that drivers are aware of drowsiness while driving, and stopping driving when feeling 'sleepy' may significantly reduce crash risk.

Using long short term memory and convolutional neural networks for driver drowsiness detection

Fatigue negatively affects the safety and performance of drivers on the road. In fact, drowsiness and fatigue are the cause of a substantial number of motor vehicle accidents. Drowsiness among the drivers can be detected using variety of modalities, including electroencephalogram (EEG), eye movement, and vehicle driving dynamics. Among these EEG is highly accurate but very intrusive and cumbersome. On the other hand, vehicle driving dynamics are very easy to acquire but accuracy is not very high. Eye movement based approach is very attractive in terms of balance between these two extremes. However, eye movement based techniques normally require an eye tracking device which consists of high speed camera with sophisticated algorithm to extract eye movement related parameters such as blinking, eye closure, saccades, fixation etc. This makes eye tracking based drowsiness detection difficult to implement as a practical system, especially on an embedded platform. In this paper, authors propose to use eye images from camera directly without the need for expensive eye-tracking system. Here, eye related movements are captured by Recurrent Neural Network (RNN) to detect the drowsiness. Long Short Term Memory (LSTM) is a class of RNN which has several advantages over vanilla RNNs. In this work an array of LSTM cells are utilized to model the eye movements. Two types of LSTMs were employed: 1-D LSTM (R-LSTM) which is used as baseline and the convolutional LSTM (C-LSTM) which facilitates using 2-D images directly. Patches of size 48 × 48 around each eye were extracted from 38 subjects, participating in a simulated driving experiment. The state of vigilance among the subjects were independently assessed by power spectral analysis of multichannel electroencephalogram (EEG) signals, recorded simultaneously, and binary labels of alert and drowsy (baseline) were generated. Results show high efficacy of the proposed system. R-LSTM based approach resulted in accuracy around 82 % and C-LSTM based approach resulted in accuracy in the range of 95%-97%. Comparison is also provided with a recently published eye-tracking based approach, showing the proposed LSTM technique outperform with a wide margin.

A novel convolutional neural network method for subject-independent driver drowsiness detection based on single-channel data and EEG alpha spindles

A significant number of fatal accidents are caused by drowsy drivers worldwide. Driver drowsiness detection based on electroencephalography (EEG) signals has high accuracy and is known as a reference method for evaluating drowsiness. Among brain waves, EEG alpha spindle activity is a silent feature of decreasing alertness levels. In this paper, based on the detection of EEG alpha spindles, a novel driver drowsiness detection method is presented. The EEG spindles were detected using Continuous Wavelet Transform (CWT) analysis and the Morlet function. To do so, the signal is divided into 30-s epochs, and the observer rating of drowsiness determines the drowsiness level in each epoch. Tests were conducted on 17 healthy males in a driving simulator with a monotonous driving scenario. The Convolutional Neural Network (CNN) is used for classifying EEG signals and automatically learns features of the early drowsy state. The subject-independent classification results for single-channel P4 show 94% accuracy.

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.

Motorcycling performance and sleepiness during an extended ride on a dynamic simulator: relationship with stress biomarkers

OBJECTIVE

Powered two-wheelers (PTW) make up a large proportion of fatal accidents. The aim of this study was to investigate the effects of time-of-day and total sleep deprivation (SD) on simulated motorcycling performance during extended riding sessions (60 min), while evaluating stress mechanisms.

APPROACH

A total of 16 healthy males participated in four simulated motorcycling sessions at 07:00, 11:00, 15:00 and 19:00, including city (8 min), country (2 min) and highway pathways (40 min), after a normal night of sleep and after total SD (30 h), in a randomized counterbalanced order. The recorded motorcycle parameters included: variation of lateral position, number of inappropriate line crossings (ILC), falls, riding errors, speed and speed limit violations. Subject parameters included the number of microsleeps in each pathway, the number of lapses during the 3-min psychomotor vigilance task (PVT-Brief version), and the Karolinska sleepiness scale (KSS) score. Saliva samples were used to assess cortisol (sC), α-amylase (sAA), and chromogranin-A (sCgA). ANOVAs and Pearson's correlation analysis were performed between these variables.

MAIN RESULTS

Most parameters were influenced by an interaction effect between 'Motorcycling pathways' × 'SD' (speed (p < 0.05), legal speed violations (p < 0.01), variation of lateral position (p < 0.001), falls (p < 0.001), EEG-microsleeps (p < 005)). An interaction effect between 'SD' × 'Time-of-day' influenced the number of ILCs (p < 0.01), sC (p < 0.05) and sCgA (p < 0.05) levels. SD affected KSS scores (p < 0.001) and PVT lapses (p < 0.05). The highest disturbances were associated with highway motorcycling simulation.

SIGNIFICANCE

Sleepiness due to circadian or SD and fatigue effects significantly affect riding and increase the risks involved with PTWs. The activation of both stress systems seems not sufficient to alleviate these deleterious effects.

Age-Related Changes in Attentional Refocusing during Simulated Driving

We recently reported that refocusing attention between temporal and spatial tasks becomes more difficult with increasing age, which could impair daily activities such as driving (Callaghan et al., 2017). Here, we investigated the extent to which difficulties in refocusing attention extend to naturalistic settings such as simulated driving. A total of 118 participants in five age groups (18–30; 40–49; 50–59; 60–69; 70–91 years) were compared during continuous simulated driving, where they repeatedly switched from braking due to traffic ahead (a spatially focal yet temporally complex task) to reading a motorway road sign (a spatially more distributed task). Sequential-Task (switching) performance was compared to Single-Task performance (road sign only) to calculate age-related switch-costs. Electroencephalography was recorded in 34 participants (17 in the 18–30 and 17 in the 60+ years groups) to explore age-related changes in the neural oscillatory signatures of refocusing attention while driving. We indeed observed age-related impairments in attentional refocusing, evidenced by increased switch-costs in response times and by deficient modulation of theta and alpha frequencies. Our findings highlight virtual reality (VR) and Neuro-VR as important methodologies for future psychological and gerontological research.

Vehicle and Highway Adaptations to Compensate for Sleepy Drivers

Sleepiness remains a major contributor to road crashes. Driver monitoring systems identify early signs of sleepiness and alert drivers, using real-time analysis of eyelid movements, EEG activity, and steering control. Other vehicle adaptations warn drivers of lane departures or collision hazards, with higher vehicle automation actively taking over vehicle control to prevent run off the road incidents and institute emergency braking. Similarly, road adaptations warn drivers (rumble strips) or mitigate crash severity (barriers). Infrastructure to encourage drivers to use countermeasures, such as rest stops for napping, is also important. The effectiveness of adaptations varies for different road users.

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.

Between-Frequency Topographical and Dynamic High-Order Functional Connectivity for Driving Drowsiness Assessment

Previous studies exploring driving drowsiness utilized spectral power and functional connectivity without considering between-frequency and more complex synchronizations. To complement such lacks, we explored inter-regional synchronizations based on the topographical and dynamic properties between frequency bands using high-order functional connectivity (HOFC) and envelope correlation. We proposed the dynamic interactions of HOFC, associated-HOFC, and a global metric measuring the aggregated effect of the functional connectivity. The EEG dataset was collected from 30 healthy subjects, undergoing two driving sessions. The two-session setting was employed for evaluating the metric reliability across sessions. Based on the results, we observed reliably significant metric changes, mainly involving the alpha band. In HOFC_θα , HOFC_αβ , associated- HOFC_θα , and associated- HOFC_αβ , the connection-level metrics in frontal-central, central-central, and central-parietal/occipital areas were significantly increased, indicating a dominance in the central region. Similar results were also obtained in the HOFC_θαβ and aHOFC_θαβ . For dynamic-low-order-FC and dynamic-HOFC, the global metrics revealed a reliably significant increment in the alpha, theta-alpha, and alpha-beta bands. Modularity indexes of associated- HOFC_α and associated- HOFC_θα also exhibited reliably significant differences. This paper demonstrated that within-band and between-frequency topographical and dynamic FC can provide complementary information to the traditional individual-band LOFC for assessing driving drowsiness.

EEG-Based Neurocognitive Metrics May Predict Simulated and On-Road Driving Performance in Older Drivers

The number of older drivers is steadily increasing, and advancing age is associated with a high rate of automobile crashes and fatalities. This can be attributed to a combination of factors including decline in sensory, motor, and cognitive functions due to natural aging or neurodegenerative diseases such as HIV-Associated Neurocognitive Disorder (HAND). Current clinical assessment methods only modestly predict impaired driving. Thus, there is a need for inexpensive and scalable tools to predict on-road driving performance. In this study EEG was acquired from 39 HIV+ patients and 63 healthy participants (HP) during: 3-Choice-Vigilance Task (3CVT), a 30-min driving simulator session, and a 12-mile on-road driving evaluation. Based on driving performance, a designation of Good/Poor (simulator) and Safe/Unsafe (on-road drive) was assigned to each participant. Event-related potentials (ERPs) obtained during 3CVT showed increased amplitude of the P200 component was associated with bad driving performance both during the on-road and simulated drive. This P200 effect was consistent across the HP and HIV+ groups, particularly over the left frontal-central region. Decreased amplitude of the late positive potential (LPP) during 3CVT, particularly over the left frontal regions, was associated with bad driving performance in the simulator. These EEG ERP metrics were shown to be associated with driving performance across participants independent of HIV status. During the on-road evaluation, Unsafe drivers exhibited higher EEG alpha power compared to Safe drivers. The results of this study are 2-fold. First, they demonstrate that high-quality EEG can be inexpensively and easily acquired during simulated and on-road driving assessments. Secondly, EEG metrics acquired during a sustained attention task (3CVT) are associated with driving performance, and these metrics could potentially be used to assess whether an individual has the cognitive skills necessary for safe driving.

Evidence of attentional impairments using virtual driving simulation in multiple sclerosis

BACKGROUND

Detection of attentional disorders in complex situation related to daily life activities in multiple sclerosis patients needs better adapted tools than traditional cognitive assessment.

OBJECTIVE

To investigate the usefulness of virtual reality assessment of attention in multiple sclerosis, especially to evaluate alertness and divided attention using driving simulation.

METHODS

In this preliminary study, 11 relapsing-remitting patients (median EDSS: 2; mean disease duration of 10.3 years) and 11 healthy matched controls performed a driving simulation under three conditions (monotonous driving, divided attention driving and urban driving) where Standard Deviation of Lateral position (SDLP) was the main evaluated criteria. In comparison, traditional cognitive assessment of attentional functions was administered (SDMT, alertness and divided attention of TAP battery). Statistical non-parametric Mann-Whitney U tests were used to compare performances between groups in the two types of assessments. Exploratory correlational analyses were further conducted.

RESULTS

No significant difference was observed between groups for traditional attentional assessment except for information processing speed (SDMT; p < 0.01). Considering virtual reality, patients were less efficient than controls on the primary parameter of safe driving (SDLP; p < 0.05). They also committed more errors and omissions (p < 0.01) and speed fluctuations (p < 0.01) during the divided-attention driving condition. Urban driving did not reveal difference between groups. Lack of significant correlations between traditional and virtual reality attentional assessment suggested that they do not evaluate the same cognitive processes.

CONCLUSION

Patients experienced difficulties in maintaining the trajectory and the speed of the simulated vehicle which may be indicative of attentional difficulties, especially alertness and divided attention. These impairments were not revealed by the traditional cognitive assessment. Results of this preliminary study shed new light about the usefulness of virtual reality techniques and their future interest as a part of cognitive rehabilitation programs. They also highlights the need to develop driving preventive measures in MS.