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

Real-time performance modelling of a sustained attention to response task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participant’s reaction times during a monotonous task. A lab-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Then relevant parameters are used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models are compared to detect in real-time – minute by minute - the lapses in vigilance during the task. We show that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables to detect vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared to Neural Networks and Generalised Linear Mixed Models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks

Using Electroencephalographic (EEG) Measures Of Working Memory In The Context Of Binge Drinking: Analyzing Theta/Gamma Ratios

Neuronal degeneration resulting from excessive alcohol use, such that occurs with binge drinking behavior, is proposed to be linked to both impairment in neuronal functioning and corresponding psychological deficits in executive functioning such as working memory. A total of 73 undergraduate students (mean age = 19) completed a series of questionnaires assessing drinking behaviors and mental health. Upon completion, subjects were assigned to one of two groups based on their reported drinking behavior. Subjects completed a series of psychomotor tasks that required working memory demands under temporal processing conditions from the Senaptec Sensory Station tablet as well as two traditional working memory computer tasks. EEG data was collected to identify differences in working memory capacity between binge drinkers and casual drinkers using a short-term memory load index (STMLI) which was calculated by dividing the power spectral density (PSD) for theta located at the Fz electrode by the corresponding PSD for gamma for each participant. The primary analyses did not show significant group differences for the STMLI on the cognitive tasks. Secondary analyses were conducted with two new groups, light drinkers vs. heavy drinkers. There was a significant group difference for the STMLI during completion of the Go/No-go task. Additional analyses were conducted with the new groups exploring differences for Fz theta, beta, and gamma power as well as POz alpha. Finally, cognitive state metric comparisons were explored between groups. There were no reported group differences seen on any of the behavioral performance measures (i.e., Go/No-go, Digit Span task) for either of the group comparisons. The findings of the current study suggest greater sensitivity of physiological measures to potential cognitive deficits associated with early alcohol consumption compared to traditional cognitive measures in this age group. Implications of this research demonstrate that negative consequences of heavy alcohol consumption can occur even in young populations and is evident through a variety of brain activity measurements during tasks of working memory

Physiological-based Driver Monitoring Systems: A Scoping Review

A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD

A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks.

Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant θ and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications

Physiological Approach To Characterize Drowsiness In Simulated Flight Operations During Window Of Circadian Low

Drowsiness is a psycho-physiological transition from awake towards falling sleep and its detection is crucial in aviation industries. It is a common cause for pilot’s error due to unpredictable work hours, longer flight periods, circadian disruption, and insufficient sleep. The pilots’ are prone towards higher level of drowsiness during window of circadian low (2:00 am- 6:00 am). Airplanes require complex operations and lack of alertness increases accidents. Aviation accidents are much disastrous and early drowsiness detection helps to reduce such accidents. This thesis studied physiological signals during drowsiness from 18 commercially-rated pilots in flight simulator. The major aim of the study was to observe the feasibility of physiological signals to predict drowsiness. In chapter 3, the spectral behavior of electroencephalogram (EEG) was studied via power spectral density and coherence. The delta power reduced and alpha power increased significantly (

Cognitive facilitation following intentional odor exposure

This paper reviews evidence that, in addition to incidental olfactory pollutants, intentional odor delivery can impact cognitive operations both positively and negatively. Evidence for cognitive facilitation/interference is reviewed alongside four potential explanations for odor-induced effects. It is concluded that the pharmacological properties of odors can induce changes in cognition. However, these effects can be accentuated/attenuated by the shift in mood following odor exposure, expectancy of cognitive effects, and cues to behavior via the contextual association with the odor. It is proposed that greater consideration is required in the intentional utilization of odors within both industrial and private locations, since differential effects are observed for odors with positive hedonic qualities

Sleep deprivation and brain energy metabolism : in vivo studies in rats and humans

Sleep deprivation leads to increased subsequent sleep length and depth and to deficits in cognitive performance in humans. In animals extreme sleep deprivation is eventually fatal. The cellular and molecular mechanisms causing the symptoms of sleep deprivation are unclear. This thesis was inspired by the hypothesis that during wakefulness brain energy stores would be depleted, and they would be replenished during sleep. The aim of this thesis was to elucidate the energy metabolic processes taking place in the brain during sleep deprivation. Endogenous brain energy metabolite levels were assessed in vivo in rats and in humans in four separate studies (Studies I-IV). In the first part (Study I) the effects of local energy depletion on brain energy metabolism and sleep were studied in rats with the use of in vivo microdialysis combined with high performance liquid chromatography. Energy depletion induced by 2,4-dinitrophenol infusion into the basal forebrain was comparable to the effects of sleep deprivation: both increased extracellular concentrations of adenosine, lactate, and pyruvate, and elevated subsequent sleep. This result supports the hypothesis of a connection between brain energy metabolism and sleep. The second part involved healthy human subjects (Studies II-IV). Study II aimed to assess the feasibility of applying proton magnetic resonance spectroscopy (1H MRS) to study brain lactate levels during cognitive stimulation. Cognitive stimulation induced an increase in lactate levels in the left inferior frontal gyrus, showing that metabolic imaging of neuronal activity related to cognition is possible with 1H MRS. Study III examined the effects of sleep deprivation and aging on the brain lactate response to cognitive stimulation. No physiologic, cognitive stimulation-induced lactate response appeared in the sleep-deprived and in the aging subjects, which can be interpreted as a sign of malfunctioning of brain energy metabolism. This malfunctioning may contribute to the functional impairment of the frontal cortex both during aging and sleep deprivation. Finally (Study IV), 1H MRS major metabolite levels in the occipital cortex were assessed during sleep deprivation and during photic stimulation. N-acetyl-aspartate (NAA/H2O) decreased during sleep deprivation, supporting the hypothesis of sleep deprivation-induced disturbance in brain energy metabolism. Choline containing compounds (Cho/H2O) decreased during sleep deprivation and recovered to alert levels during photic stimulation, pointing towards changes in membrane metabolism, and giving support to earlier observations of altered brain response to stimulation during sleep deprivation. Based on these findings, it can be concluded that sleep deprivation alters brain energy metabolism. However, the effects of sleep deprivation on brain energy metabolism may vary from one brain area to another. Although an effect of sleep deprivation might not in all cases be detectable in the non-stimulated baseline state, a challenge imposed by cognitive or photic stimulation can reveal significant changes. It can be hypothesized that brain energy metabolism during sleep deprivation is more vulnerable than in the alert state. Changes in brain energy metabolism may participate in the homeostatic regulation of sleep and contribute to the deficits in cognitive performance during sleep deprivation.Valvotus lisää korvausunen määrää ja johtaa ihmisillä kognitiivisen suorituskyvyn heikkenemiseen. Eläimillä on havaittu äärimmilleen pitkitetyn valvotuksen johtavan lopulta kuolemaan. Unen puutteen aiheuttamien oireiden taustalla olevat solu- ja molekyylitason mekanismit tunnetaan puutteellisesti. Väitöskirja Sleep deprivation and brain energy metabolism in vivo studies in rats and humans on saanut innoituksensa hypoteesista, jonka mukaan aivojen energiavarastot ehtyisivät valveen ja palautuisivat ennalleen unen aikana. Työssä tutkittiin aivojen energia-aineenvaihdunnan muutoksia valvotuksen aikana rotilla ja ihmisillä. Ensimmäisessä osatyössä tutkittiin aivojen paikallisen energiavajeen vaikutuksia rottien uneen ja aivojen energia-aineenvaihduntaan. Kokeellinen energiavaje etuaivojen pohjaosissa oli verrattavissa unen puutteen vaikutuksiin: molemmat aiheuttivat energia-aineenvaihduntatuotteiden (adenosiinin, laktaatin ja pyruvaatin) solunulkoisten pitoisuuksien kasvua sekä korvausunen lisääntymistä. Muissa osatöissä tutkittiin ihmisiä. Toisessa osatyössä todettiin protonispektroskopialla (1H MRS) kognitiivisen tehtävän suorittamisen nostavan terveiden aivojen laktaattipitoisuutta paikallisesti vasemmassa otsalohkossa (ns. laktaattivaste). Kolmannessa osatyössä todettiin, että tämä laktaattivaste ei tule esiin ikääntyvillä eikä valvotetuilla koehenkilöillä. Voidaan tulkita, että laktaattivasteen puuttuminen johtuu normaalin energia-aineenvaihdunnan häiriintymisestä. Pitkittyneen valveen sekä ikääntymisen aikana havaitut otsalohkon toiminnan häiriöt saattavat osin selittyä tämän havainnon pohjalta. Viimeisessä osatyössä todettiin näköaivokuoren N-asetyyliaspartaattipitoisuuden laskevan unen puutteen aikana, mikä tukee hypoteesia unen puutteen aikaisesta aivojen energia-aineenvaihdunnan häiriöstä. Myös koliiniyhdisteiden määrä näköaivokuorella laski unen puutteen aikana mutta palautui lähtötasolle näköärsytyksen myötä. Jälkimmäinen havainto viittaa solukalvojen aineenvaihdunnan muutoksiin unen puutteen aikana ja tukee aiempia havaintoja aivojen ärsytysvasteen muuttumisesta valvotetuilla koehenkilöillä. Yhteenvetona väitöskirjatyön tulosten perusteella voidaan päätellä, että valvominen muuttaa aivojen energia-aineenvaihduntaa. Unen puutteen vaikutus aivojen energia-aineenvaihduntaan voi kuitenkin vaihdella eri aivoalueiden välillä. Vaikka muutos ei aina tulekaan ilmi lepotilassa, se voi ilmetä kognitiivisen tehtävän suorittamisen tai näköärsytyksen aikana. Voidaan olettaa, että aivojen energia-aineenvaihdunta on unen puutteen aikana haavoittuvaisempi kuin virkeänä. Aivojen energia-aineenvaihdunnan muutokset saattavat osallistua unen säätelyyn sekä vaikuttaa kognitiivisen suorituskyvyn heikkenemiseen unen puutteen aikana

Optimising the training-induced changes of inhibitory control

In four studies, this thesis examined the effect of task difficulty and brief training on inhibitory processing in the Go/Nogo task, and transfer to the Stop-signal and Eriksenflanker tasks. It also aimed to clarify how the event-related potential (ERP) of the N2 and P3, as well as the earlier N1 and P2 components, reflect training-related modulations in the underlying neural processes. This was achieved by (1) the use of three task difficulty levels (Low, Medium, High) using incremental reaction time deadlines (RTDs), (2) the effect of these three RTDs on task performance and the early (N1, P2) and inhibition-related (N2, P3) ERP components after brief training, (3) the use of another form of task difficulty – stimulus prepotency – to investigate whether training effects may be enhanced, and (4) the use of single Go/Nogo training (planned inhibition) vs. combined training of Go/Nogo (planned inhibition) and Stop-signal (action cancellation) inhibition. The main results were that the Nogo N2 effect was robustly observed to increase with greater task difficulty (i.e. RTDs), but that it reduced irrespective with time-on-task or training condition. It does not appear to reflect neural processing related to motor or pre-motor inhibition, but may instead represent the detection of conflict between responses. The Nogo P3, however, behaved in a fashion consistent with an inhibitory interpretation, being reduced with greater task difficulty (concurrent with lower levels of task performance), but showing increased amplitudes over frontal brain regions with training and improved task performance – an effect that showed near-transfer to an untrained Stop-signal task. Reduced N1, but enhanced P2 amplitudes, occurred regardless of training condition, indicating a generalised change in sensory processing with repeated task administration. The results cast doubt on the current inhibitory interpretation of the N2. Instead they suggest that, not only does the amplitude of the frontocentral Nogo P3 represent neural processing related to inhibitory control, but that it shows clear training-induced quantitative changes coinciding with performance improvements - furthering both the theoretical and applied knowledge of the key task parameters required to effectively train inhibitory control