Assessment of early onset of driver fatigue using multimodal fatigue measures in a static simulator

Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments

BACKGROUND

Driving fatigue is a significant concern in contemporary society, contributing to a considerable number of traffic accidents annually. This study explores novel methods for fatigue detection, aiming to enhance driving safety.

METHODS

This study utilizes electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to monitor driver fatigue during simulated driving experiments lasting up to 7 h.

RESULTS

Analysis reveals a significant correlation between behavioral data and hemodynamic changes in the prefrontal lobe, particularly around the 4 h mark, indicating a critical period for driver performance decline. Despite a small participant cohort, the study's outcomes align closely with established fatigue standards for drivers.

CONCLUSIONS

By integrating fNIRS into non-voluntary attention brain function experiments, this research demonstrates promising efficacy in accurately detecting driving fatigue. These findings offer insights into fatigue dynamics and have implications for shaping effective safety measures and policies in various industrial settings.

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.

Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic Review

BACKGROUND

This review systematically examined the scientific literature about electroencephalogram-derived ratio indexes used to assess human mental involvement, in order to deduce what they are, how they are defined and used, and what their best fields of application are. (2) Methods: The review was carried out according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. (3) Results: From the search query, 82 documents resulted. The majority (82%) were classified as related to mental strain, while 12% were classified as related to sensory and emotion aspects, and 6% to movement. The electroencephalographic electrode montage used was low-density in 13%, high-density in 6% and very-low-density in 81% of documents. The most used electrode positions for computation of involvement indexes were in the frontal and prefrontal cortex. Overall, 37 different formulations of involvement indexes were found. None of them could be directly related to a specific field of application. (4) Conclusions: Standardization in the definition of these indexes is missing, both in the considered frequency bands and in the exploited electrodes. Future research may focus on the development of indexes with a unique definition to monitor and characterize mental involvement.

Integrated Thermofluid Lumped Parameter Model for Analyzing Hemodynamics in Human Fatigue State

It is well known that driving while fatigued is dangerous and can lead to serious traffic accidents. However, there is a lack of studies on the mechanism of fatigue. This paper sought to infer changes in the cardiovascular system through hand and head skin temperature peripheral factors via an integrated lumped parameter model. A multi-layer inner structure with variable blood perfusion was used to construct a full-body thermal model. The cardiovascular system model provided blood perfusion using lumped parameters. The peripheral resistance and heart rate in the cardiovascular system model were adjusted to match the experimental temperatures of the head and hands obtained from induced fatigue experiments. The simulation results showed that the heart rate and blood pressure decreased, and the peripheral skin resistance of the hands and head increased after fatigue. A decrease in heart rate and an increase in peripheral resistance affect the magnitude of blood flow to the periphery of the body, leading to a decrease in skin temperature during fatigue. The present integrated model elucidates a key effect of human fatigue on the cardiovascular system, which is expected to help improve the accuracy of fatigue monitoring systems.

Perceived discomfort and neuromuscular fatigue during long-duration real driving with different car seats

INTRODUCTION

Identification of the seat features that could improve driving experience is a main issue for automotive companies.

OBJECTIVE

Long duration real driving sessions were performed to assess the effect of three seats (soft-S1, firm-S2 and suspended-S3) on perceived discomfort and neuromuscular fatigue (NMF).

MATERIALS & METHODS

For each seat, the muscular activity of bilateral Trapezius Descendens (TD), Erector Spinae (ES) and Multifidus (MF) muscles of twenty-one participants was recorded during real driving sessions of 3-hours each lasting approximately 3 hours and following the same itinerary. During each driving session, participants were also regularly asked to self-evaluate their level of whole-body and local discomfort. In addition, an endurance static test (EST) was performed before (ESTpre) and after (ESTpost) each driving session to assess the seat effect on physical capacity.

RESULTS

Whole-body discomfort increased with driving time for all seats, but this increase became significant latter for S3. The highest scores of local discomfort occurred for neck and lower back. Contrary to S1 and S2, the duration of ESTpost was not significantly lower compared to ESTpre with the S3. Interestingly, muscular activity of S1 remained stable throughout the driving task which could be attributed to sustained muscular contraction, while muscular recruitment adjustments occurred for S2 and S3 from 1H00 of driving. This muscular compensation concerns mostly the right side for S2 and S3 but with different profiles. On the left side, the muscular adjustments concern only the MF with S2 and the ES with S3.

CONCLUSION

Overall, our results demonstrated that S3 could be considered as the most suitable seat to delay discomfort and NMF appearance.

Development of Modular and Adaptive Laboratory Set-Up for Neuroergonomic and Human-Robot Interaction Research

The industry increasingly insists on academic cooperation to solve the identified problems such as workers' performance, wellbeing, job satisfaction, and injuries. It causes an unsafe and unpleasant working environment that directly impacts the quality of the product, workers' productivity, and effectiveness. This study aimed to give a specialized solution for tests and explore possible solutions to the given problem in neuroergonomics and human–robot interaction. The designed modular and adaptive laboratory model of the industrial assembly workstation represents the laboratory infrastructure for conducting advanced research in the field of ergonomics, neuroergonomics, and human–robot interaction. It meets the operator's anatomical, anthropometric, physiological, and biomechanical characteristics. Comparing standard, ergonomic, guided, and collaborative work will be possible based on workstation construction and integrated elements. These possibilities allow the industry to try, analyze, and get answers for an identified problem, the condition, habits, and behavior of operators in the workplace. The set-up includes a workstation with an industry work chair, a Poka–Yoke system, adequate lighting, an audio 5.0 system, containers with parts and tools, EEG devices (a cap and smartfones), an EMG device, touchscreen PC screen, and collaborative robot. The first phase of the neuroergonomic study was performed according to the most common industry tasks defined as manual, monotonous, and repetitive activities. Participants have a task to assemble the developed prototype model of an industrial product using prepared parts and elements, and instructed by the installed touchscreen PC. In the beginning, the participant gets all the necessary information about the experiment and gets 15 min of practice. After the introductory part, the EEG device is mounted and prepared for recording. The experiment starts with relaxing music for 5 min. The whole experiment lasts two sessions per 60 min each, with a 15 min break between the sessions. Based on the first experiments, it is possible to develop, construct, and conduct complex experiments for industrial purposes to improve the physical, cognitive, and organizational aspects and increase workers' productivity, efficiency, and effectiveness. It has highlighted the possibility of applying modular and adaptive ergonomic research laboratory experimental set-up to transform standard workplaces into the workplaces of the future.

Influence of car seat firmness on seat pressure profiles and perceived discomfort during prolonged simulated driving

During a driving task, the seat-driver interface is particularly influenced by the external environment and seat features. This study compares the effect of two different seats (S₁ - soft & S₂ - firm) and the effect of visual simulation of different road types (city, highway, mountain, country), on pressure distribution and perceived discomfort during prolonged driving. Twenty participants drove two 3-h sessions (one per seat) on a static simulator. Contact Pressure (CP), Contact Surface (CS), and Seat Pressure Distribution Percentage (SPD%) were analyzed throughout, using two pressure mats positioned on seat cushion and backrest. Whole-body and local discomfort for each body part were rated every 20 min. The softer seat, S₁, induced a greater contact surface on cushion and backrest and a lower SPD%, reflecting better pressure distribution. Pressure profiles were asymmetrical for both S₁ and S_2, with higher CP under left buttock (LBu) and right lower back (RLb) and greater CS under thighs and RLb. Pressure distribution was less homogeneous on mountain and city roads than on monotonous roads (highway and country). Despite the pressure differences between the seats, however, both led to similar increases in perceived whole-body discomfort throughout the driving session. Moreover, the highest discomfort scores were in the neck and the lower back areas, whatever the seat. These findings on pressure variables may have implications for the design of backrests and cushions to ensure more homogeneous pressure distribution, even though this is not shown to minimize perceived driver discomfort.

Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review

The competitive demand for attention is present in our daily lives, and the identification of neural processes in the EEG signals associated with the demand for specific attention can be useful to the individual’s interactions in virtual environments. Since EEG-based devices can be portable, non-invasive, and present high temporal resolution technology for recording neural signal, the interpretations of virtual systems user’s attention, fatigue and cognitive load based on parameters extracted from the EEG signal are relevant for several purposes, such as games, rehabilitation, and therapies. However, despite the large amount of studies on this subject, different methodological forms are highlighted and suggested in this work, relating virtual environments, demand of attention, workload and fatigue applications. In our summarization, we discuss controversies, current research gaps and future directions together with the background and final sections.

Car seat impact on driver's sitting behavior and perceived discomfort during prolonged real driving on varied road types

Prolonged driving under real conditions can entail discomfort linked to driving posture, seat design features, and road properties like whole-body vibrations (WBV). This study evaluated the effect of three different seats (S1 = soft; S2 = firm; S3 = soft with suspension system) on driver's sitting behavior and perceived discomfort on different road types in real driving conditions. Twenty-one participants drove the same 195 km itinerary alternating highway, city, country, and mountain segments. Throughout the driving sessions, Contact Pressure (CP), Contact Surface (CS), Seat Pressure Distribution Percentage (SPD%) and Repositioning Movements (RM) were recorded via two pressure mats installed on seat cushion and backrest. Moreover every 20 minutes, participants rated their whole-body and local discomfort. While the same increase in whole-body discomfort with driving time was observed for all three seats, S3 limited local perceived discomfort, especially in buttocks, thighs, neck, and upper back. The pressure profiles of the three seats were similar for CP, CS and RM on the backrest but differed on the seat cushion. The soft seats (S1 & S3) showed better pressure distribution, with lower SPD% than the firm seat (S2). All three showed highest CP and CS under the thighs. Road type also affected both CP and CS of all three seats, with significant differences appearing between early city, highway and country segments. In the light of these results, automotive manufacturers could enhance seat design for reduced driver discomfort by combining a soft seat cushion to reduce pressure peaks, a firm backrest to support the trunk, and a suspension system to minimize vibrations.

Electrophysiological frequency domain analysis of driver passive fatigue under automated driving conditions

With the continuous improvement of automated vehicles, researchers have found that automated driving is more likely to cause passive fatigue. To explore the impact of automation and scenario complexity on the passive fatigue of a driver, we collected electroencephalography (EEG), detection-response task (DRT) performance, and the subjective report scores of 48 drivers. We found that in automated driving under monotonic conditions, after 40 min, the alpha power of the driver’s EEG indicators increased significantly, the accuracy of the detection reaction task decreased, and the reaction time became slower. The receiver characteristic curve was used to calculate the critical threshold of the alpha power during passive fatigue. The determination of the threshold further clarifies the occurrence time and physiological characteristics of passive fatigue and improves the passive fatigue theory.

Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation

Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation.

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.

Changes in EEG permutation entropy in the evening and in the transition from wake to sleep

Quantifying the complexity of the EEG signal during prolonged wakefulness and during sleep is gaining interest as an additional mean to characterize the mechanisms associated with sleep and wakefulness regulation. Here, we characterized how EEG complexity, as indexed by Multiscale Permutation Entropy (MSPE), changed progressively in the evening prior to light off and during the transition from wakefulness to sleep. We further explored whether MSPE was able to discriminate between wakefulness and sleep around sleep onset and whether MSPE changes were correlated with spectral measures of the EEG related to sleep need during concomitant wakefulness (theta power-Ptheta: 4-8 Hz). To address these questions, we took advantage of large datasets of several hundred of ambulatory EEG recordings of individual of both sexes aged 25-101 years. Results show that MSPE significantly decreases before light off (i.e. before sleep time) and in the transition from wakefulness to sleep onset. Furthermore, MSPE allows for an excellent discrimination between pre-sleep wakefulness and early sleep. Finally, we show that MSPE is correlated with concomitant Ptheta. Yet, the direction of the latter correlation changed from before light-off to the transition to sleep. Given the association between EEG complexity and consciousness, MSPE may track efficiently putative changes in consciousness preceding sleep onset. An MSPE stands as a comprehensive measure that is not limited to a given frequency band and reflects a progressive change brain state associated with sleep and wakefulness regulation. It may be an effective mean to detect when the brain is in a state close to sleep onset.

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

BACKGROUND

Neuroergonomics combines neuroscience with ergonomics to study human performance using recorded brain signals. Such neural signatures of performance can be measured using a variety of neuroimaging techniques, including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG). EEG has an excellent temporal resolution, and EEG indices are highly sensitive to human brain activity fluctuations.

OBJECTIVE

The focus of this systematic review was to explore the applications of EEG indices for quantifying human performance in a variety of cognitive tasks at the macro and micro scales. To identify trends and the state of the field, we examined global patterns among selected articles, such as journal contributions, highly cited papers, affiliations, and high-frequency keywords. Moreover, we discussed the most frequently used EEG indices and synthesized current knowledge regarding the EEG signatures of associated human performance measurements.

METHODS

In this systematic review, we analyzed articles published in English (from peer-reviewed journals, proceedings, and conference papers), Ph.D. dissertations, textbooks, and reference books. All articles reviewed herein included exclusively EEG-based experimental studies in healthy participants. We searched Web-of-Science and Scopus databases using specific sets of keywords.

RESULTS

Out of 143 papers, a considerable number of cognitive studies focused on quantifying human performance with respect to mental fatigue, mental workload, mental effort, visual fatigue, emotion, and stress. An increasing trend for publication in this area was observed, with the highest number of publications in 2017. Most studies applied linear methods (e.g., EEG power spectral density and the amplitude of event-related potentials) to evaluate human cognitive performance. A few papers utilized nonlinear methods, such as fractal dimension, largest Lyapunov exponent, and signal entropy. More than 50% of the studies focused on evaluating an individual's mental states while operating a vehicle. Several different methods of artifact removal have also been noted. Based on the reviewed articles, research gaps, trends, and potential directions for future research were explored.

CONCLUSION

This systematic review synthesized current knowledge regarding the application of EEG indices for quantifying human performance in a wide variety of cognitive tasks. This knowledge is useful for understanding the global patterns of applications of EEG indices for the analysis and design of cognitive tasks.

Neuromuscular fatigue profiles depends on seat feature during long duration driving on a static simulator

Prolonged driving could induce neuromuscular fatigue and discomfort since drivers have little opportunity to adjust their position. However, better car seat design could play a major role in limiting these effects. This study compared the effect of two different seats (S - soft and F - firm) on neuromuscular fatigue and driver's perceived discomfort during prolonged driving, also assessing the effect of different road types on neuromuscular activity. Twenty participants performed two 3-h driving sessions, one for each seat, on a static simulator. Every 20 min, participants self-evaluated their level of whole-body and individual body-area discomfort. Surface electromyography (sEMG) was recorded for eight muscles including Trapezius descendens (TD), Erector spinae longissimus (ESL), Multifidus (MF), Vastus lateralis (VL) and Tibialis anterior (TA) throughout the driving sessions. Moreover, an endurance static test (EST) was performed prior to and after each driving session. Whole-body discomfort increased with time with both seats, but no difference in discomfort scores was observed between seats throughout the driving sessions. The highest discomfort scores were for neck and lower back areas with both seats. Neuromuscular fatigue was revealed by a shorter endurance time in post-driving EST for both seats. EMG recordings showed different neuromuscular fatigue profiles for the two seats, with earlier onset of fatigue for S. Despite the lack of difference in perceived discomfort level, the two seats have different impacts: the softness of S induces greater activity of the lower back muscles, while F offers greater support for the lower back.

Assessment of the static upright balance index and brain blood oxygen levels as parameters to evaluate pilot workload

Objective

To investigate the potential for static upright balance function and brain-blood oxygen parameters to evaluate pilot workload.

Methods

Phase 1: The NASA Task Load Index (NASA-TLX) was used to compare the workloads of real flights with flight simulator simulated flight tasks in 15 pilots (Cohort 1). Phase 2: To determine the effects of workload, 50 cadets were divided equally into simulated flight task load (experimental) and control groups (Cohort 2). The experimental group underwent 2 h of simulated flight tasks, while the control group rested for 2 h. Their static upright balance function was evaluated using balance index-1 (BI-1), before and after the tasks, with balance system posturography equipment and cerebral blood oxygen parameters monitored with near infrared spectroscopy (NIRS) in real time. Sternberg dual-task and reaction time tests were performed in the experimental and control groups before and after the simulated flight tasks.

Results

(Phase1) There was a significant correlation between the workload caused by real flight and simulated flight tasks (P<0.01), indicating that NASA-TLX scales were also a tool for measuring workloads of the stimulated flight tasks. (Phase 2) For the simulated flight task experiments, the NASA-TLX total scores were significantly different between the two groups (P<0.001) and (pre-to-post) changes of the BI-1 index were greater in the experimental group than in controls (P<0.001). The cerebral blood oxygen saturation levels (rsO₂) (P<0.01) and ΔHb reductions (P<0.05) were significantly higher in the experimental, compared to the control group, during the simulated flight task. In contrast to the control group the error rates (P = 0.002) and accuracy (P<0.001) changed significantly in the experimental group after the simulated flight tasks.

Conclusions

The simulated flight task model could simulate the real flight task load and static balance and NIRS were useful for evaluating pilots’ workload/fatigue.

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.

Engineering movement into automotive seating: Does the driver feel more comfortable and refreshed?

The concept of introducing movement in automotive seating was investigated. Three seat conditions, a control (no movement) and two movement conditions (fore-aft and cushion-backrest) were compared. Movement was introduced at a fixed speed, slow, smooth and within a small range. Ten participants took part in a 60 min simulated drive for each condition - single blind, repeated measures, and balanced order. Discomfort ratings were collected for six body areas and overall discomfort, together with a wellbeing questionnaire. Driver posture and Seat Fidgets and Movements (SFMs) were captured. There was a trend for lower ratings of discomfort, overall and in the neck, shoulders, lower back, buttocks, and ankles with both seat movement conditions. Wellbeing ratings were also better with movement. Significant differences were found at minute 60 for buttock discomfort - less discomfort with seat movement. Overall discomfort and SFMs frequency increased with time driving. Generally, passive seat movement was well received.

Fatigue in Long-Haul Truck Drivers: A Concept Analysis

The majority of long-haul truck drivers are affected by fatigue. Notably, fatigue impacts not only quality of life, but the risk for human error, thus impacting the safety of our nation’s roads. In 2015, 400,000 trucking accidents occurred with the top driver impairment identified as being “asleep or fatigued.” Yet, the current scientific literature reflects different understandings of the concept of fatigue, hindering synchronized measurement and efforts toward its alleviation. The purpose of this work was to analyze the concept of fatigue in long-haul truck drivers using the Rodgers’ evolutionary method. The physical, cognitive, and emotional dimensions of fatigue are presented.

Analysis of Feature Fatigue EEG Signals Based on Wavelet Entropy

Fatigue driving is bringing more and more serious harm, but there are various reasons for fatigue driving, it is still difficult to test the driver’s fatigue. This paper defines a method to test driver’s fatigue based on the EEG, and different from other researches into fatigue driving, this paper mainly takes the fatigue features of EEG signals in fatigue state and uses wavelet entropy as the feature extraction method to analyze the features of wavelet entropy and spectral entropy features as well as the classification accuracy under the same classifier. The SVM is used to show the classifier’s results. The accuracy of the driver fatigue state monitoring using the wavelet entropy is 90.7%, which is higher than the use of spectral entropy as the characteristic accuracy rate of 81.3%. The results show that the frequency characteristics of EEG can be well applied to driving fatigue testing, but different frequency feature calculation methods will affect the classification accuracy.

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1–2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.

Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload

Many people suffer from high mental workload which may threaten human health and cause serious accidents. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. Different physiological signals have been used to estimate mental workload based on the n-back task which is capable of inducing different mental workload levels. This paper explores a feature weight driven signal fusion method and proposes interactive mutual information modeling (IMIM) to increase the mental workload classification accuracy. We used EEG and ECG signals to validate the effectiveness of the proposed method for heterogeneous bio-signal fusion. The experiment of mental workload estimation consisted of signal recording, artifact removal, feature extraction, feature weight calculation, and classification. Ten subjects were invited to take part in easy, medium and hard tasks for the collection of EEG and ECG signals in different mental workload levels. Therefore, heterogeneous physiological signals of different mental workload states were available for classification. Experiments reveal that ECG can be utilized as a supplement of EEG to optimize the fusion model and improve mental workload estimation. Classification results show that the proposed bio-signal fusion method IMIM can increase the classification accuracy in both feature level and classifier level fusion. This study indicates that multi-modal signal fusion is promising to identify the mental workload levels and the fusion strategy has potential application of mental workload estimation in cognitive activities during daily life.

The effect of physical fatigue on oscillatory dynamics of the sensorimotor cortex

AIM

While physical fatigue is known to arise in part from supraspinal mechanisms within the brain, exactly how brain activity is modulated during fatigue is not well understood. Therefore, this study examined how typical neural oscillatory responses to voluntary muscle contractions were affected by fatigue.

METHODS

Eleven healthy adults (age 27 ± 4 years) completed two experimental sessions in a randomized crossover design. Both sessions first assessed baseline maximal voluntary isometric wrist-flexion force (MVF_b ). Participants then performed an identical series of fourteen test contractions (2 × 100%MVF_b , 10 × 40%MVF_b , 2 × 100%MVF_b ) both before and after one of two interventions: forty 12-s contractions at 55%MVF_b (fatigue intervention) or 5%MVF_b (control intervention). Magnetoencephalography (MEG) was used to characterize both the movement-related mu and beta decrease (MRMD and MRBD) and the post-movement beta rebound (PMBR) within the contralateral sensorimotor cortex during the 40%MVF_b test contractions, while the 100%MVF_b test contractions were used to monitor physical fatigue.

RESULTS

The fatigue intervention induced a substantial physical fatigue that endured throughout the post-intervention measurements (28.9-29.5% decrease in MVF, P < 0.001). Fatigue had a significant effect on both PMBR (anova, session × time-point interaction: P = 0.018) and MRBD (P = 0.021): the magnitude of PMBR increased following the fatigue but not the control interventions, whereas MRBD was decreased post-control but not post-fatigue. Mu oscillations were unchanged throughout both sessions.

CONCLUSION

Physical fatigue resulted in an increased PMBR, and offset attenuations in MRBD associated with task habituation.

Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals

An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%).

Determination of the Proper Rest Time for a Cyclic Mental Task Using ACT-R Architecture

Objective Analysis of the effect of mental fatigue on a cognitive task and determination of the right start time for rest breaks in work environments. Background Mental fatigue has been recognized as one of the most important factors influencing individual performance. Subjective and physiological measures are popular methods for analyzing fatigue, but they are restricted to physical experiments. Computational cognitive models are useful for predicting operator performance and can be used for analyzing fatigue in the design phase, particularly in industrial operations and inspections where cognitive tasks are frequent and the effects of mental fatigue are crucial. Method A cyclic mental task is modeled by the ACT-R architecture, and the effect of mental fatigue on response time and error rate is studied. The task includes visual inspections in a production line or control workstation where an operator has to check products' conformity to specifications. Initially, simulated and experimental results are compared using correlation coefficients and paired t test statistics. After validation of the model, the effects are studied by human and simulated results, which are obtained by running 50-minute tests. Results It is revealed that during the last 20 minutes of the tests, the response time increased by 20%, and during the last 12.5 minutes, the error rate increased by 7% on average. Conclusion The proper start time for the rest period can be identified by setting a limit on the error rate or response time. Application The proposed model can be applied early in production planning to decrease the negative effects of mental fatigue by predicting the operator performance. It can also be used for determining the rest breaks in the design phase without an operator in the loop.

Towards continuous and real-time attention monitoring at work: reaction time versus brain response

Continuous and objective measurement of the user attention state still represents a major challenge in the ergonomics research. Recently available wearable electroencephalography (EEG) opens new opportunities for objective and continuous evaluation of operators' attention, which may provide a new paradigm in ergonomics. In this study, wearable EEG was recorded during simulated assembly operation, with the aim to analyse P300 event-related potential component, which provides reliable information on attention processing. In parallel, reaction times (RTs) were recorded and the correlation between these two attention-related modalities was investigated. Negative correlation between P300 amplitudes and RTs has been observed on the group level (p < .001). However, on the individual level, the obtained correlations were not consistent. As a result, we propose the P300 amplitude for accurate attention monitoring in ergonomics research. On the other hand, no significant correlation between RTs and P300 latency was found on group, neither on individual level. Practitioner Summary: Ergonomic studies of assembly operations mainly investigated physical aspects, while mental states of the assemblers were not sufficiently addressed. Presented study aims at attention tracking, using realistic workplace replica. It is shown that drops in attention could be successfully traced only by direct brainwave observation, using wireless electroencephalographic measurements.

Single-channel EEG-based mental fatigue detection based on deep belief network

Mental fatigue has a pernicious influence on road and work place safety as well as a negative symptom of many acute and chronic illnesses, since the ability of concentrating, responding and judging quickly decreases during the fatigue or drowsiness stage. Electroencephalography (EEG) has been proven to be a robust physiological indicator of human cognitive state over the last few decades. But most existing EEG-based fatigue detection methods have poor performance in accuracy. This paper proposed a single-channel EEG-based mental fatigue detection method based on Deep Belief Network (DBN). The fused nonliear features from specified sub-bands and dynamic analysis, a total of 21 features are extracted as the input of the DBN to discriminate three classes of mental state including alert, slight fatigue and severe fatigue. Experimental results show the good performance of the proposed model comparing with those state-of-art methods.

Multi-parameter prediction of drivers' lane-changing behaviour with neural network model

Accurate prediction of driving behaviour is essential for an active safety system to ensure driver safety. A model for predicting lane-changing behaviour is developed from the results of naturalistic on-road experiment for use in a lane-changing assistance system. Lane changing intent time window is determined via visual characteristics extraction of rearview mirrors. A prediction index system for left lane changes was constructed by considering drivers' visual search behaviours, vehicle operation behaviours, vehicle motion states, and driving conditions. A back-propagation neural network model was developed to predict lane-changing behaviour. The lane-change-intent time window is approximately 5 s long, depending on the subjects. The proposed model can accurately predict drivers' lane changing behaviour for at least 1.5 s in advance. The accuracy and time series characteristics of the model are superior to the use of turn signals in predicting lane-changing behaviour.