Applications of brain imaging methods in driving behaviour research

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

Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding.

Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals. Then, an unsupervised clustering is conducted on the extracted feature space to identify the brain (external and covert) states that respond to different cognitive demands. To understand the human state change, a state transition diagram is introduced, allowing visualization of connectivity patterns between every pair of states. We compute the transition probability between every pair of states to represent the relationships between the states. This state transition diagram is named as the Fuzzy Covert State Transition Diagram (FCOSTD), which helps the understanding of human states and human performance. We then apply FCOSTD on distracted driving experiments. FCOSTD successfully discovers the external and covert states, faithfully reveals the transition of the brain between states, and the route of the state change when humans are distracted during a driving task. The experimental results demonstrate that different subjects have similar states and inter-state transition behaviour (establishing the consistency of the system) but different ways to allocate brain resources as different actions are being taken

Exploring the Brain Responses to Driving Fatigue through Simultaneous EEG and fNIRS Measurements

© 2020 World Scientific Publishing Company. Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO2) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO2 decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO2 oscillations suggest that activations in HbO2 are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain's responses as evidence of state changes during fatigue driving

Brain functional connectivity in unconstrained walking with and without an exoskeleton

An exoskeleton is utilized to effectively restore the motor function of amputees’ limbs and is frequently employed in motor rehabilitation training during convalescence. Understanding of exoskeleton impact on the brain is required in order to better and more efficiently use the exoskeleton. Almost all previous studies investigated the exoskeleton effect on the brain in a situation with constraints such as predefined walking speed, which could lead to findings differed from that obtained in an unconstrained situation. We, therefore, performed an experiment of unconstrained walking with and without an exoskeleton. Both individual connections and graph metrics were explored and compared among walking conditions. We found that low-order functional connections and associated high-order functional connections mainly between the left centroparietal region and right frontal region were significantly different among walking conditions. Generally speaking, connective strength was enhanced in LOFC and was decreased in aHOFC when assistant force was provided by the exoskeleton. Further, we proposed connection length investigation and revealed the large majority of these connections were long-distance connectivity. Graph metric investigation discovered higher connectivity clustering in the walking with low exoskeleton-aided force compared to the walking without the exoskeleton. This study expanded the existing knowledge of the effect of exoskeleton on the brain and is of implications on new exoskeleton development and exoskeleton-aided rehabilitation training

Theta and alpha oscillations in attentional interaction during distracted driving

© 2018 Wang, Jung and Lin. Performing multiple tasks simultaneously usually affects the behavioral performance as compared with executing the single task. Moreover, processing multiple tasks simultaneously often involve more cognitive demands. Two visual tasks, lane-keeping task and mental calculation, were utilized to assess the brain dynamics through 32-channel electroencephalogram (EEG) recorded from 14 participants. A 400-ms stimulus onset asynchrony (SOA) factor was used to induce distinct levels of attentional requirements. In the dual-task conditions, the deteriorated behavior reflected the divided attention and the overlapping brain resources used. The frontal, parietal and occipital components were decomposed by independent component analysis (ICA) algorithm. The event- and response-related theta and alpha oscillations in selected brain regions were investigated first. The increased theta oscillation in frontal component and decreased alpha oscillations in parietal and occipital components reflect the cognitive demands and attentional requirements as executing the designed tasks. Furthermore, time-varying interactive over-additive (O-Add), additive (Add) and under-additive (U-Add) activations were explored and summarized through the comparison between the summation of the elicited spectral perturbations in two single-task conditions and the spectral perturbations in the dual task. Add and U-Add activations were observed while executing the dual tasks. U-Add theta and alpha activations dominated the posterior region in dual-task situations. Our results show that both deteriorated behaviors and interactive brain activations should be comprehensively considered for evaluating workload or attentional interaction precisely

Master of Science

thesisMedia-related technology can capture attention, leaving the user depleted of attentional resources. However, the theory of attention restoration (ART) suggests that environments with certain qualities can restore previously depleted resources. Previous research has tested ART using a variety of behavioral and perceptual tasks; however, researchers have yet to examine the predictions of ART using neurophysiological methods. I hypothesize that the default mode network, a region associated with internal thoughts, is associated with the restoration process in nature. This exploratory study evaluated the process of restoration using electroencephalography to measure potential changes in oscillatory activity after participants were exposed to a natural environment. Forty-seven (19 males) participants 18 to 37 years in age were recruited from the Salt Lake City area via flyers, advertisements, and word of mouth. In order to assess the effects of technological distraction, participants were assigned to a group that was concurrently talking on a phone during the walk or to a group that did not have any technology with them. Participants in the phone group had decreased recognition memory and increased activity at the theta frequency after the walk compared to participants who went on the walk without their phone. The data indicate that technology disrupts the process of restoration and decreases awareness of the surroundings

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

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

Refining the Assessment of ADHD: The Relationship Between Self-Report and Observable Behavioral Symptoms

Individuals with adult attention-deficit/hyperactivity disorder (ADHD) present with deficits in attention, hyperactivity-impulsivity, or a combination of these symptoms. Functional impairment due to inattention, reduced motivation, poor impulse control, emotion dysregulation, and deficits in executive functioning are also frequently seen. Assessment of ADHD, using objective continuous performance tasks, has been introduced in conjunction with widely used self-report measures of ADHD symptom severity. The Conner’s Adult ADHD Rating Scale (CAARS) is one such self-report measure of ADHD, consisting of 66 self-report items that measure symptoms of ADHD, including inattention, hyperactivity, impulsivity, and poor self-concept. The Quotient ADHD System purports to provide objective measures of ability to inhibit motor activity, maintain attention, and suppress impulsive responses. The Quotient ADHD System has been found to be sensitive to the pharmacologic effects of ADHD medication. Currently, there is a lack of empirical evidence for the Quotient ADHD System in an adult population, specifically, indicating a relationship between evaluations using the Quotient ADHD System and a widely accepted self-report measurement of ADHD, such as the CAARS. The present study determined that the global scaled score and motion scaled score metrics of the Quotient ADHD System correlate with the Hyperactive/Restlessness scale on the CAARS. Furthermore, the present study found a significant positive correlation between the Inattentive Metric of the Quotient ADHD System and the Inattention/Memory scale on the CAARS

Using electroencephalography to analyse drivers’ different cognitive workload characteristics based on on-road experiment

Driver’s cognitive workload has an important impact on driving safety. This paper carries out an on-road experiment to analyse the impact from three innovative aspects: significance analysis of electroencephalogram (EEG) under different cognitive workloads, distribution of EEG maps with different frequency signals and influence of different cognitive workloads on driving safety based on EEG. First, the EEG signals are processed and four frequencies of delta, theta, alpha and beta are obtained. Then, the time–frequency transform and power spectral density calculation are carried out by short-time Fourier to study the correlation of each frequency signal of different workload states, as well as the distribution pattern of the EEG topographic map. Finally, the time and space energy and phase changes in each cognitive task event are studied through event-related spectral perturbation and inter-trial coherence. Results show the difference between left and right brains, as well as the resource occupancy trends of the monitor, perception, visual and auditory channels in different driving conditions. Results also demonstrate that the increase in cognitive workloads will directly affect driving safety. Changes in cognitive workload have different effects on brain signals, and this paper can provide a theoretical basis for improving driving safety under different cognitive workloads. Mastering the EEG characteristics of signals can provide more targeted supervision and safety warnings for the driver