Vigilance States Research Articles

Hypnotic effects of melatonin depend on the environmental lighting conditions in the rat.

Acute effects of exogenous melatonin have been widely reported to promote sleep or induce drowsiness in human. However, testing of the hypnotic effects of melatonin in nocturnal rodents has yielded contradictory results. The latter may be associated with differences in concentration, lighting conditions, time of administration of melatonin, and possibly the type of analysis. In this study, electroencephalogram (EEG) and electromyogramwere recorded in pigmented male Brown Norway rats under both light-dark (LD) and constant dark (DD) conditions. Melatonin was administered intraperitoneally at a moderate dose of 3 mg/kg, at either 1 h after lights on under LD condition or 1 h after the activity offset under DD condition. The dosage is known to be able to entrain nocturnal rodents in DD conditions, but does not change sleep in rodents in LD. Only the rats under DD conditions showed a significant reduction in nonrapid eye movement (NREM) sleep latency, while the NREM sleep power spectrum remained unaffected. Under LD condition, melatonin did not alter NREM and rapid eye movement (REM) sleep latency, and had only minor effects on the NREM sleep EEG. Regardless of lighting conditions, melatonin administration resulted in less, but longer episodes for all vigilance states suggesting increased vigilance state consolidation. In the discussion, we compare our results with a summary of previously published data concerning the hypnotic effects of melatonin in polysomnographic/EEG-confirmedsleep in humans and nocturnal rodents. In conclusion, the hypnotic effect of exogenous melatonin in nocturnal rodents not only depends on the time of day, and concentration, but is also influenced by the lighting conditions. Regardless of inducing sleep or not, melatonin may consolidate sleep and through that enhance sleep quality.

Rule of Law Guarantee for Ecological Security of Inner Mongolian Grassland Based on Big Data Analysis

Abstract This paper carries out an environmental law big data analysis using big data computing based on the legislative framework of biodiversity in Inner Mongolia grassland. For the research and analysis of the rule of law guarantee for the ecological security of the Inner Mongolian grassland, it first establishes the early warning of the ecological security of the Inner Mongolian grassland through the gray prediction model and verifies the accuracy and feasibility of the prediction model precision. According to the early warning results of the ecological security of the Inner Mongolian grassland, it is suggested to enhance the legalization of grassland ecological compensation. The R 2 GM (1, 1) prediction model of Inner Mongolia grassland reaches 0.86, which shows that the prediction accuracy is good. The ecological security evaluation of Inner Mongolia from 2025 to 2035 presents, in terms of type, a safer state region > safer state region > mildly alerted state region > moderately alerted state region > seriously alerted state, and the percentage of each grade increases or decreases. Without distinguishing occupations, 93.3%-98.6% of all herders and grassland managers supported that grassland ecological compensation should be legalized. This shows that the construction of the rule of law guarantee for grassland ecological security in Inner Mongolia is an inevitable trend of development.

Effects of Nasal Respiratory Support on Laryngeal and Esophageal Reflexes in Preterm Lambs.

Significant cardiorespiratory events can be triggered in preterm infants as part of laryngeal chemoreflexes (LCRs) and esophageal reflexes (ERs). We previously showed that nasal continuous positive airway pressure (nCPAP) blunted the cardiorespiratory inhibition induced with LCRs. Therefore, we aimed to compare the effects of nCPAP and high-flow nasal cannulas (HFNC) on the cardiorespiratory events induced during LCRs and ERs. The hypothesis is that nCPAP but not HFNC decreases the cardiorespiratory inhibition observed during LCRs and ERs. Eleven preterm lambs were instrumented to record respiration, ECG, oxygenation, and states of alertness. LCRs and ERs were induced during non-rapid eye movement sleep in a random order under these conditions: nCPAP 6 cmH2O, HFNC 7 L/min, high-flow nasal cannulas 7 L/min at a tracheal pressure of 6 cmH2O, and no respiratory support. nCPAP 6 cmH2O decreased the cardiorespiratory inhibition induced with LCRs, but not with ERs in preterm lambs. This blunting effect was less marked with HFNC 7 L/min, even when the tracheal pressure was maintained at 6 cmH2O. nCPAP might be a treatment for cardiorespiratory events related to LCRs in newborns, either in the context of laryngopharyngeal refluxes or swallowing immaturity. Our preclinical results merit to be confirmed through clinical studies. Laryngeal chemoreflexes can be responsible for significant cardiorespiratory inhibition in newborns, especially preterm. Nasal continuous positive airway pressure at 6 cmH2O significantly decreased this cardiorespiratory inhibition. High-flow nasal cannulas at 7 L/min had a lesser effect than nasal continuous positive airway pressure. Esophageal stimulation was responsible for a smaller cardiorespiratory inhibition, which was not significantly modified by nasal continuous positive airway pressure or high-flow nasal cannulas. Nasal continuous positive airway pressure should be tested for its beneficial effect on cardiorespiratory events related to laryngeal chemoreflexes in preterm newborns.

On the fractal-fractional Mittag-Leffler model of a COVID-19 and Zika Co-infection

The World Health Organization declared COVID-19 a global pandemic in March 2020, which had a significant impact on global health and economies. There have been several Zika outbreaks in different regions such as Africa, Southeast Asia, and the Americas. Therefore, it is essential to study the dynamics of these two diseases, taking into account their memory and recurrence effects. A new fractal-fractional hybrid Mittag-Leffler model of COVID-19 and Zika co-dynamics is designed and studied to evaluate the effects of COVID-19 on Zika and vice-versa. The stability analysis of the local asymptotic type at disease-free equilibrium is conducted for the hybrid model. The existence of unique solutions to the model is established via some fixed point results. The fractal-fractional model is proved to be Hyers–Ulam stable. With the help of Newton polynomials, we obtain some numerical algorithms to approximate the solutions of the fractal-fractional hybrid Mittag-Leffler model graphically. The impact of fractional and fractal orders on the dynamics of each of the epidemiological classes is also assessed. In addition, empirical evidence from numerical simulations suggests that implementing measures to contain the transmission of the SARS-CoV-2 virus can significantly contribute to the reduction of co-infections involving the Zika virus. Therefore, it is imperative for healthcare systems to maintain a state of constant vigilance in order to detect any atypical patterns or probable occurrences of co-infections, particularly in areas where both diseases are widespread. Additionally, it is vital to consult the most recent directives provided by health authorities, as our comprehension of diseases may undergo advancements over the course of time.

Sleep and the hypothalamus.

Neural substrates of wakefulness, rapid eye movement sleep (REMS), and non-REMS (NREMS) in the mammalian hypothalamus overlap both anatomically and functionally with cellular networks that support physiological and behavioral homeostasis. Here, we review the roles of sleep neurons of the hypothalamus in the homeostatic control of thermoregulation or goal-oriented behaviors during wakefulness. We address how hypothalamic circuits involved in opposing behaviors such as core body temperature and sleep compute conflicting information and provide a coherent vigilance state. Finally, we highlight some of the key unresolved questions and challenges, and the promise of a more granular view of the cellular and molecular diversity underlying the integrative role of the hypothalamus in physiological and behavioral homeostasis.

O072 Simple Vestibular-Occular Motor Assessment as a Predictor of Alertness State and Driving Impairment during Extended Wakefulness

Abstract Introduction Driver fatigue contributes to 2-16% of road crashes, highlighting the need for improved detection of at-risk drivers. We used a novel and brief test of vestibular ocular motor system (VOMS) assessed via virtual reality goggles to predict alertness state and driving simulator performance during 29hr extended wakefulness. Methods 49 individuals (Mean±SD Age, 32.6±12.9, 45% Males) undergone 9hr baseline sleep opportunity followed by ~29hrs extended wakefulness with five 60min driving assessments. Cluster analysis, combining steering deviation and number of crashes were used to split participants into groups of either poor vs good driving performance. VOMS assessment was conducted using virtual reality goggles approximately 10mins before and after each drive. Predictive importance of VOMs metrics were ranked using XGBoost machine learning model, which was then utilized to distinguish between poor vs good driving groups. Model performance was evaluated using a 5-fold cross-validation approach using ROC analysis. Results XGBoost machine learning ranked all 70 VOMS metrics on their importance in predicting driving performance group for each drive. Top 10 metrics from pre-drive VOMS test predicted both daytime driving (tests 1-3, AUC 0.8 [95%CI 0.64-0.93], p<0.001) and night-time driving (tests 4-5, AUC 0.78 [95%CI 0.6-0.95, p<0.001]). Post-driving VOMS assessments also predicted daytime (AUC 0.74 [95%CI 0.53-0.9, p<0.001] and night-time driving (AUC 0.76 [95%CI 0.52-0.94, p<0.001]). Conclusion VOMS assessment show promise as a short and effective assessment of sleepiness to predict driving failure. Future validation in independent samples, sleep disordered population and in-field on-road testing are needed to confirm these promising findings.

Radial astrocyte synchronization modulates the visual system during behavioral-state transitions

Glial cells support the function of neurons. Recent evidence shows that astrocytes are also involved in brain computations. To explore whether and how their excitable nature affects brain computations and motor behaviors, we used two-photon Ca2+ imaging of zebrafish larvae expressing GCaMP in both neurons and radial astrocytes (RAs). We found that in the optic tectum, RAs synchronize their Ca2+ transients immediately after the end of an escape behavior. Using optogenetics, ablations, and a genetically encoded norepinephrine sensor, we observed that RA synchronous Ca2+ events are mediated by the locus coeruleus (LC)-norepinephrine circuit. RA synchronization did not induce direct excitation or inhibition of tectal neurons. Nevertheless, it modulated the direction selectivity and the long-distance functional correlations among neurons. This mechanism supports freezing behavior following a switch to an alerted state. These results show that LC-mediated neuro-glial interactions modulate the visual system during transitions between behavioral states.

Individualized Prediction of Task Performance Decline Using Pre-Task Resting-State Functional Connectivity.

As a common complaint in contemporary society, mental fatigue is a key element in the deterioration of the daily activities known as time-on-task (TOT) effect, making the prediction of fatigue-related performance decline exceedingly important. However, conventional group-level brain-behavioral correlation analysis has the limitation of generalizability to unseen individuals and fatigue prediction at individual-level is challenging due to the significant differences between individuals both in task performance efficiency and brain activities. Here, we introduced a cross-validated data-driven analysis framework to explore, for the first time, the feasibility of utilizing pre-task idiosyncratic resting-state functional connectivity (FC) on the prediction of fatigue-related task performance degradation at individual level. Specifically, two behavioral metrics, namely ∆RT (between the most vigilant and fatigued states) and TOTslope over the course of the 15-min sustained attention task, were estimated among three sessions from 37 healthy subjects to represent fatigue-related individual behavioral impairment. Then, a connectome-based prediction model was employed on pre-task resting-state FC features, identifying the network-related differences that contributed to the prediction of performance deterioration. As expected, prominent populational TOT-related performance declines were revealed across three sessions accompanied with substantial inter-individual differences. More importantly, we achieved significantly high accuracies for individualized prediction of both TOT-related behavioral impairment metrics using pre-task neuroimaging features. Despite the distinct patterns between both behavioral metrics, the identified top FC features contributing to the individualized predictions were mainly resided within/between frontal, temporal and parietal areas. Overall, our results of individualized prediction framework extended conventional correlation/classification analysis and may represent a promising avenue for the development of applicable techniques that allow precaution of the TOT-related performance declines in real-world scenarios.

The politics of attention in the paintings of René Tavares

René Tavares’s artistic production actively engages with political themes like migration and racism, simultaneously reflecting and inviting the deconstruction of the ‘alert attentional regime’. This term refers to our digital era's characteristic heightened state of alertness and fragmented attention, which scatters our focus across multiple streams of information. Rather than simply mirroring society’s fragmented attentional state, his work fosters more mindful and intentional engagement, encouraging viewers to transcend the transient attentiveness inherent in contemporary society. Central to his practice is the concept of rhythm, symbolizing the velocity of modern life and facilitating a shift to more sustained attention. This is achieved through patterns and colours that mimic swift information cycles, inherent to our times, captivating the viewer’s attention while setting the stage for a deeper, more mindful engagement with the artwork. Tavares intertwines elements like the ‘floating mark’, representing ephemeral meanings in our digital era; ‘polyfocality’, reflecting and addressing society’s fragmented attention through multiple focal points, and ‘pictorial syncretism’, which refers to the amalgamation of different painterly registers such as stains, graphic registers, splatters, layer overlay, word insertion and scrapings, blending visual traditions to highlight the fluid cultural boundaries in our globalized world. By doing so, it potentially opens up the possibility for a redistribution of the sensible, prompting critical thinking and an inherently political understanding of our place within a broader societal context.

The differing effects of sleep on ictal and interictal network dynamics in drug-resistant epilepsy.

Sleep has important influences on focal interictal epileptiform discharges (IEDs), and the rates and spatial extent of IEDs are increased in non-rapid eye movement (NREM) sleep. In contrast, the influence of sleep on seizures is less clear and its effects on seizure topography are poorly documented. We evaluated the influences of NREM sleep on ictal spatiotemporal dynamics and contrasted these with interictal network dynamics. We included patients with drug-resistant focal epilepsy who underwent continuous intracranial EEG with depth electrodes. Patients were selected if they had 1-3 seizures from each vigilance state, wakefulness and NREM sleep, within a 48-hour window and under the same anti-seizure medication. A 10-min epoch of the interictal iEEG was selected per state, and IEDs were detected automatically. Twenty-five patients (13 females; 32.5±7.1 years) were included. The seizure onset pattern, duration, spatiotemporal propagation and latency of ictal high-frequency activity did not differ significantly between wakefulness and NREM sleep (all p>0.05). In contrast, IED rates and spatial distribution were increased in NREM compared to wakefulness (p<0.001, Cliff's d=0.48 and 0.49). The spatial overlap between vigilance states was higher for seizures (57.1±40.1%) than IEDs (41.7±46.2%) (p=0.001, Cliff's d=0.51). In contrast to its effects on IEDs, NREM sleep does not affect ictal spatiotemporal dynamics. This suggests that once the brain surpasses the seizure threshold, it will follow the underlying epileptic network irrespective of the vigilance state. These findings offer valuable insights into neural network dynamics in epilepsy and have important clinical implications for localizing seizure foci. This article is protected by copyright. All rights reserved.

Inducing and measuring acute stress in virtual reality: Evaluation of canonical physiological stress markers and measuring methods

Besides entertainment, Virtual Reality (VR) is becoming increasingly important for professional applications. VR offers a wide range of use, from simulating and dealing with critical or dangerous situations or procedures, competence-oriented learning and testing in an educational context, diagnostic or therapeutic applications in the medical field, or even for tele manipulators and robotics. In most of the mentioned scenarios, measuring the alertness, mental load, stress or relaxation state of the user would be a clear enhancement. That information can serve as a valuable input to control or influence the VR-setting (e.g. for biofeedback) or lead to additional information, for instance as an countable outcome of a simulation or for objectifiable results that can be used for debriefing or diagnostics. Since stress mirrors a neurophysiological response, it is possible to measure the reaction of the human body to a stressor. This leads to the activation of the autonomous nervous system that controls many different physical reactions. To find the best parameter measuring the timing of a sympathetic activation to a predefined stressor (especially with regards to our research of detecting substance craving precisely after a virtual exposure for a VR-based therapy system in addiction medicine), electroencephalography (EEG), heart rate variability (HRV), skin conductance response (SCR), and peripheral skin temperature (ST) were compared against each other regarding a real-time detection of stress among criteria such as the costs for building a wireless sensor, the ease of use in a professional, medical or home use and the robustness against artifacts and the precision of the measurement. We used a 180-degree horror movie as a stressor for a healthy study population and took care about possible confounders, that may influence the subject's stress level or the measurements. All measurement procedures and the used devices complied with medical standards. It was possible to provoke a measurable physiological stress reaction in all subjects through VR, although not all participants subjectively felt scared or stressed. EEG, SCR and peripheral skin temperature turned out to be equally well suited for real-time detection of stress. HRV failed to reliably show the stress response, which does not disqualify this measuring method, but might rather be connected with measurement timeframes and data preparation in our study. When it comes to the ease of use, the robustness of the measurement, costs and the possibility to implement the measuring technique within a wireless system (e.g., finger clips or even the VR hand controllers), we consider the skin conductance response (SCR) as the most promising physiological parameter to objectify stress in a virtual environment.

Ketogenic Diet Affects Sleep Architecture in C57BL/6J Wild Type and Fragile X Mice.

Nearly half of children with fragile X syndrome experience sleep problems including trouble falling asleep and frequent nighttime awakenings. The goals here were to assess sleep-wake cycles in mice in response to Fmr1 genotype and a dietary intervention that reduces hyperactivity. Electroencephalography (EEG) results were compared with published rest-activity patterns to determine if actigraphy is a viable surrogate for sleep EEG. Specifically, sleep-wake patterns in adult wild type and Fmr1KO littermate mice were recorded after EEG electrode implantation and the recordings manually scored for vigilance states. The data indicated that Fmr1KO mice exhibited sleep-wake patterns similar to wild type littermates when maintained on a control purified ingredient diet. Treatment with a high-fat, low-carbohydrate ketogenic diet increased the percentage of non-rapid eye movement (NREM) sleep in both wild type and Fmr1KO mice during the dark cycle, which corresponded to decreased activity levels. Treatment with a ketogenic diet flattened diurnal sleep periodicity in both wild type and Fmr1KO mice. Differences in several sleep microstructure outcomes (number and length of sleep and wake bouts) supported the altered sleep states in response to a ketogenic diet and were correlated with altered rest-activity cycles. While actigraphy may be a less expensive, reduced labor surrogate for sleep EEG during the dark cycle, daytime resting in mice did not correlate with EEG sleep states.

Reducing brain kynurenic acid synthesis precludes kynurenine-induced sleep disturbances.

Patients with neurocognitive disorders often battle sleep disturbances. Kynurenic acid is a tryptophan metabolite of the kynurenine pathway implicated in the pathology of these illnesses. Modest increases in kynurenic acid, an antagonist at glutamatergic and cholinergic receptors, result in cognitive impairments and sleep dysfunction. We explored the hypothesis that inhibition of the kynurenic acid synthesising enzyme, kynurenine aminotransferase II, may alleviate sleep disturbances. At the start of the light phase, adult male and female Wistar rats received systemic injections of either: (i) vehicle; (ii) kynurenine (100 mg kg-1; i.p.); (iii) the kynurenine aminotransferase II inhibitor, PF-04859989 (30 mg kg-1; s.c.); or (iv) PF-04859989 and kynurenine in combination. Kynurenine and kynurenic acid levels were evaluated in the plasma and brain. Separate animals were implanted with electroencephalogram and electromyogram telemetry devices to record polysomnography, and evaluate the vigilance states wake, rapid eye movement sleep and non-rapid eye movement sleep following each treatment. Kynurenine challenge increased brain kynurenic acid and resulted in reduced rapid eye movement sleep duration, non-rapid eye movement sleep delta power and sleep spindles. PF-04859989 reduced brain kynurenic acid formation when given prior to kynurenine, prevented disturbances in rapid eye movement sleep and sleep spindles, and enhanced non-rapid eye movement sleep. Our findings suggest that reducing kynurenic acid in conditions where the kynurenine pathway is activated may serve as a potential strategy for improving sleep dynamics.

Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems

Objective. The state of vigilance is crucial for effective performance in brain-computer interface (BCI) tasks, and therefore, it is essential to investigate vigilance levels in BCI tasks. Despite this, most studies have focused on vigilance levels in driving tasks rather than on BCI tasks, and the electroencephalogram (EEG) patterns of vigilance states in different BCI tasks remain unclear. This study aimed to identify similarities and differences in EEG patterns and performances of vigilance estimation in different BCI tasks and sessions. Approach. To achieve this, we built a steady-state visual evoked potential-based BCI system and a rapid serial visual presentation-based BCI system and recruited 18 participants to carry out four BCI experimental sessions over four days. Main results. Our findings demonstrate that specific neural patterns for high and low vigilance levels are relatively stable across sessions. Differential entropy features significantly differ between different vigilance levels in all frequency bands and between BCI tasks in the delta and theta frequency bands, with the theta frequency band features playing a critical role in vigilance estimation. Additionally, prefrontal, temporal, and occipital regions are more relevant to the vigilance state in BCI tasks. Our results suggest that cross-session vigilance estimation is more accurate than cross-task estimation. Significance. Our study clarifies the underlying mechanisms of vigilance state in two BCI tasks and provides a foundation for further research in vigilance estimation in BCI applications.

Neonatal cortical activity organizes into transient network states that are affected by vigilance states and brain injury

Early neurodevelopment is critically dependent on the structure and dynamics of spontaneous neuronal activity; however, the natural organization of newborn cortical networks is poorly understood. Recent adult studies suggest that spontaneous cortical activity exhibits discrete network states with physiological correlates. Here, we studied newborn cortical activity during sleep using hidden Markov modeling to determine the presence of such discrete neonatal cortical states (NCS) in 107 newborn infants, with 47 of them presenting with a perinatal brain injury. Our results show that neonatal cortical activity organizes into four discrete NCSs that are present in both cardinal sleep states of a newborn infant, active and quiet sleep, respectively. These NCSs exhibit state-specific spectral and functional network characteristics. The sleep states exhibit different NCS dynamics, with quiet sleep presenting higher fronto-temporal activity and a stronger brain-wide neuronal coupling. Brain injury was associated with prolonged lifetimes of the transient NCSs, suggesting lowered dynamics, or flexibility, in the cortical networks. Taken together, the findings suggest that spontaneously occurring transient network states are already present at birth, with significant physiological and pathological correlates; this NCS analysis framework can be fully automatized, and it holds promise for offering an objective, global level measure of early brain function for benchmarking neurodevelopmental or clinical research.

The effect of sleep restriction, with or without high-intensity interval exercise, on behavioural alertness and mood state in young healthy males.

Mood state and alertness are negatively affected by sleep loss, and can be positively influenced by exercise. However, the potential mitigating effects of exercise on sleep-loss-induced changes in mood state and alertness have not been studied comprehensively. Twenty-four healthy young males were matched into one of three, 5-night sleep interventions: normal sleep (NS; total sleep time (TST) per night = 449 ± 22 min), sleep restriction (SR; TST = 230 ± 5 min), or sleep restriction and exercise (SR + EX; TST = 235 ± 5 min, plus three sessions of high-intensity interval exercise (HIIE)). Mood state was assessed using the profile of mood states (POMS) and a daily well-being questionnaire. Alertness was assessed using psychomotor vigilance testing (PVT). Following the intervention, POMS total mood disturbance scores significantly increased for both the SR and SR + EX groups, and were greater than the NS group (SR vs NS; 31.0 ± 10.7 A.U., [4.4-57.7 A.U.], p = 0.020; SR + EX vs NS; 38.6 ± 14.9 A.U., [11.1-66.1 A.U.], p = 0.004). The PVT reaction times increased in the SR (p = 0.049) and SR + EX groups (p = 0.033) and the daily well-being questionnaire revealed increased levels of fatigue in both groups (SR; p = 0.041, SR + EX; p = 0.026) during the intervention. Despite previously demonstrated physiological benefits of performing three sessions of HIIE during five nights of sleep restriction, the detriments to mood, wellness, and alertness were not mitigated by exercise in this study. Whether alternatively timed exercise sessions or other exercise protocols could promote more positive outcomes on these factors during sleep restriction requires further research.

The maturation of aperiodic EEG activity across development reveals a progressive differentiation of wakefulness from sleep

During development, the brain undergoes radical structural and functional changes following a posterior-to-anterior gradient, associated with profound changes of cortical electrical activity during both wakefulness and sleep. However, a systematic assessment of the developmental effects on aperiodic EEG activity maturation across vigilance states is lacking, particularly regarding its topographical aspects. Here, in a population of 160 healthy infants, children and teenagers (from 2 to 17 years, 10 subjects for each year), we investigated the development of aperiodic EEG activity in wakefulness and sleep. Specifically, we parameterized the shape of the aperiodic background of the EEG Power Spectral Density (PSD) by means of the spectral exponent and offset; the exponent reflects the rate of exponential decay of power over increasing frequencies and the offset reflects an estimate of the y-intercept of the PSD.We found that sleep and development caused the EEG-PSD to rotate over opposite directions: during wakefulness the PSD showed a flatter decay and reduced offset over development, while during sleep it showed a steeper decay and a higher offset as sleep becomes deeper. During deep sleep (N2, N3) only the spectral offset decreased over age, indexing a broad-band voltage reduction. As a result, the difference between values in deep sleep and those in both light sleep (N1) and wakefulness increased with age, suggesting a progressive differentiation of wakefulness from sleep EEG activity, most prominent over the frontal regions, the latest to complete maturation. Notably, the broad-band spectral exponent values during deep sleep stages were entirely separated from wakefulness values, consistently across developmental ages and in line with previous findings in adults. Concerning topographical development, the location showing the steepest PSD decay and largest offset shifted from posterior to anterior regions with age. This shift, particularly evident during deep sleep, paralleled the migration of sleep slow wave activity and was consistent with neuroanatomical and cognitive development.Overall, aperiodic EEG activity distinguishes wakefulness from sleep regardless of age; while, during development, it reveals a postero-anterior topographical maturation and a progressive differentiation of wakefulness from sleep. Our study could help to interpret changes due to pathological conditions and may elucidate the neurophysiological processes underlying the development of wakefulness and sleep.

Driver vigilance detection based on deep learning with fused thermal image information for public transportation

Autonomous-rail Rapid Transit (ART) is a new type of road public transportation with an automatic driving function, and it is one of the main places for epidemic spread. Scientific wearing of masks has been proven to be an extremely effective way to prevent an epidemic. However, drivers wearing masks make it difficult for traditional driver alert detection methods based on facial information to operate effectively. This paper proposes an ART driver alert detection system based on multimodal information fusion and deep learning, which can be applied to drivers wearing masks. The detection system is mainly divided into three modules: data acquisition module, preprocessing module, and vigilance detection module. The data acquisition module collects real-time face thermal imaging data, and environment temperature information of 13 people in the vigilance and fatigue state of the ART simulated driving environment. The preprocessing module proposes a face thermal imaging key information extraction method based on the constraints between Yolov5 and thermal image interframe constraints and extracts the mean temperature information and the hot voxel information (HV) of the eyes, forehead, and mask areas. Then the extracted information and the environment temperature information are transmitted together to the vigilance detection module for learning and classification. The vigilance detection module proposes a lightweight hybrid-attention optimization network, and this paper extracts training sets and testing sets from the data of 13 subjects. And the training set data is enhanced. Each sample is 90 frames. The maximum accuracy rate of the testing set reaches 99.57%. The feasibility of the detection system proposed in this paper is proved, which makes the thermal imaging and attention model show great potential in the application of traffic safety operation and maintenance.

Driver Vigilance Detection Based on Limited EEG Signals

Driver vigilance detection is an important topic for traffic safety improvement and brain-computer interface design. Electroencephalography (EEG) can directly provide real-time information and is sensitive to the change of vigilance. Thus, EEG signals are widely used for driver vigilance detection. Previous studies commonly acquired EEG signals from a large number of electrode channels and utilized all the collected data for recognition tasks, which may introduce a large amount of noise and redundant data and limit their application potentials in practical systems. This paper examines the potential of using EEG signals from only one frequency band or from only a small subset of related electrode channels in recognizing driver vigilance state. The widely used SEED VIG dataset is adopt for experiments. Four candidate sets of EEG frequency bands and four candidate sets of EEG electrode channels are determined according to the statistical significance results of EEG power spectral density (PSD) features and differential entropy (DE) features. The experimental results show that the recognition accuracy is higher when using EEG signals from the selected frequency band (i.e., alpha band) or the selected electrodes (i.e., T7, TP7, CP1) than when using all the data. Specifically, the best recognition accuracy is 91.31% for the alpha band and 76.81% for the selected electrodes. These results indicate that higher driver vigilance recognition accuracy can be achieved with much less amount of data, which would facilitate the development of wearable equipment based on EEG signals.

Drivers’ drowsiness detection based on an optimized random forest classification and single-channel electroencephalogram

&lt;p&gt;The state of functioning (posture) of a driver at the wheel of a car involves a complex set of psychological, physiological, and physical parameters. This combination induces fatigue, which manifests itself in repeated yawning, stinging eyes, a frozen gaze, a stiff and painful neck, back pain, and other signs. The driver may fight fatigue for a few moments, but it inevitably leads to drowsiness, periods of micro-sleep, and then falling asleep. At the first signs of drowsiness, the risk of an accident becomes immense. In Morocco, drowsiness at the wheel is the cause of 1/3 of fatal accidents on the freeways. Thus, in this paper, a new hybrid data analysis and an efficient machine learning algorithm are designed to detect the drowsiness of drivers who spend most of their time behind the wheel over long distances (older than 35 years). This analysis is based on a single channel of electroencephalogram (EEG) recordings using time, frequency fast Fourier transform (FFT), and power spectral density (PSD) analysis. To distinguish between the two states of alertness and drowsiness, several features were extracted from each domain (time, FFT, and PSD), and subjected to different classifier architectures to conduct a general comparison and achieve the highest detection accuracy (98.5%) and best time consumption (13 milliseconds).&lt;/p&gt;