Evidence for modulation of EEG microstates by mental workload levels and task types.

Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

The mental workload of ICU nurses performing human-machine tasks and associated factors: A cross-sectional questionnaire survey.

AIMS: To assess the level of mental workload (MWL) of intensive care unit (ICU) nurses in performing different human-machine tasks and examine the predictors of the MWL. DESIGN: A cross-sectional questionnaire study. METHODS: Between January and February 2021, data were collected from ICU nurses (n = 427) at nine tertiary hospitals selected from five (east, west, south, north, central) regions in China through an electronic questionnaire, including sociodemographic questions, the National Aeronautics and Space Administration Task Load Index, General Self-Efficacy Scale, Difficulty-assessing Index System of Nursing Operation Technique, and System Usability Scale. Descriptive statistics, t-tests, one-way ANOVA and multiple linear regression models were used. RESULTS: ICU nurses experienced a medium level of MWL (score 52.04 on a scale of 0-100) while performing human-machine tasks. ICU nurses' MWL was notably higher in conducting first aid and life support tasks (using defibrillators or ventilators). Predictors of MWL were task difficulty, system usability, professional title, age, self-efficacy, ICU category, and willingness to study emerging technology actively. Task difficulty and system usability were the strongest predictors of nearly all typical tasks. CONCLUSION: ICU nurses experience a medium MWL while performing human-machine tasks, but higher mental, temporal, and effort are perceived compared to physical demands. The MWL varied significantly across different human-machine tasks, among which are significantly higher: first aid and life support and information-based human-machine tasks. Task difficulty and system availability are decisive predictors of MWL. IMPACT: This is the first study to investigate the level of MWL of ICU nurses performing different representative human-machine tasks and to explore its predictors, which provides a reference for future research. These findings suggest that healthcare organizations should pay attention to the MWL of ICU nurses and develop customized management strategies based on task characteristics to maintain a moderate level of MWL, thus enabling ICU nurses to perform human-machine tasks better. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Rapid Mental Workload Detection of Air Traffic Controllers with Three EEG Sensors.

Air traffic controllers' mental workload significantly impacts their operational efficiency and safety. Detecting their mental workload rapidly and accurately is crucial for preventing aviation accidents. This study introduces a mental workload detection model for controllers based on power spectrum features related to gamma waves. The model selects the feature with the highest classification accuracy, ß + θ + α + γ, and utilizes the mRMR (Max-Relevance and Min-Redundancy) algorithm for channel selection. Furthermore, the channels that were less affected by ICA processing were identified, and the reliability of this result was demonstrated by artifact analysis brought about by EMG, ECG, etc. Finally, a model for rapid mental workload detection for controllers was developed and the detection rate for the 34 subjects reached 1, and the accuracy for the remaining subjects was as low as 0.986. In conclusion, we validated the usability of the mRMR algorithm in channel selection and proposed a rapid method for detecting mental workload in air traffic controllers using only three EEG channels. By reducing the number of EEG channels and shortening the data processing time, this approach simplifies equipment application and maintains detection accuracy, enhancing practical usability.

Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review.

Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches.

The Impact of Various Cockpit Display Interfaces on Novice Pilots' Mental Workload and Situational Awareness: A Comparative Study.

Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and attention must be paid to the mental workload (MWL) experienced by operating pilots. If left unaddressed, a state of mental overload could affect the pilot's ability to complete his or her work activities in a safe and correct manner. This study examines the impact of two different cockpit display interfaces (CDIs), the Steam Gauge panel and the G1000 Glass panel, on novice pilots' MWL and situational awareness (SA) in a flight simulator-based setting. A combination of objective (EEG and HRV) and subjective (NASA-TLX) assessments is used to assess novice pilots' cognitive states during this study. Our results indicate that the gauge design of the CDI affects novice pilots' SA and MWL, with the G1000 Glass panel being more effective in reducing the MWL and improving SA compared with the Steam Gauge panel. The results of this study have implications for the design of future flight deck interfaces and the training of future pilots.

Assessment of Drivers' Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios.

Assessing drivers' mental workload is crucial for reducing road accidents. This study examined drivers' mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers' mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers' mental states.

EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment.

High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.

A Review of Cognitive Support Systems in the Operating Room.

BACKGROUND: In recent years, numerous innovative yet challenging surgeries, such as minimally invasive procedures, have introduced an overwhelming amount of new technologies, increasing the cognitive load for surgeons and potentially diluting their attention. Cognitive support technologies (CSTs) have been in development to reduce surgeons' cognitive load and minimize errors. Despite its huge demands, it still lacks a systematic review. METHODS: Literature was searched up until May 21st, 2021. Pubmed, Web of Science, and IEEExplore. Studies that aimed at reducing the cognitive load of surgeons were included. Additionally, studies that contained an experimental trial with real patients and real surgeons were prioritized, although phantom and animal studies were also included. Major outcomes that were assessed included surgical error, anatomical localization accuracy, total procedural time, and patient outcome. RESULTS: A total of 37 studies were included. Overall, the implementation of CSTs had better surgical performance than the traditional methods. Most studies reported decreased error rate and increased efficiency. In terms of accuracy, most CSTs had over 90% accuracy in identifying anatomical markers with an error margin below 5 mm. Most studies reported a decrease in surgical time, although some were statistically insignificant. DISCUSSION: CSTs have been shown to reduce the mental workload of surgeons. However, the limited ergonomic design of current CSTs has hindered their widespread use in the clinical setting. Overall, more clinical data on actual patients is needed to provide concrete evidence before the ubiquitous implementation of CSTs.

Factors influencing fatigue, mental workload and burnout among Chinese health care workers during public emergencies: an online cross-sectional study.

OBJECTIVES: The purpose of this study was to investigate fatigue, mental workload, and burnout among health care workers (HCWs) and explore the possible underlying factors. MATERIALS AND METHODS: An online cross-sectional survey design was used to collect data from HCWs in Chongqing, China. The online survey included the Fatigue Severity Scale, NASA Task Load Index, and Chinese version of the Maslach Burnout Inventory-General Survey to assess fatigue, mental workload, and burnout, respectively, and was conducted from February 1 to March 1, 2023. RESULTS: In this study, the incidence of fatigue and burnout among HCWs was 76.40% and 89.14%, respectively, and the incidence of moderate to intolerable mental workloads was 90.26%. Work-family conflict, current symptoms, number of days of COVID-19 positivity, mental workload, burnout and reduced personal accomplishment were significantly associated with fatigue. Mental workload was affected by fatigue and reduced personal accomplishment. Furthermore, burnout was influenced by marital status and fatigue. Moreover, there was a correlation among mental workload, fatigue, and burnout. CONCLUSIONS: Fatigue, mental workload and burnout had a high incidence and were influenced by multiple factors during COVID-19 public emergencies in China.

Relationship between mental workload and musculoskeletal disorders with intention to leave service among nurses working at neonatal and pediatric departments: a cross-sectional study in Iran.

BACKGROUND: Despite the challenge of nursing shortage in the world and its subsequent impact on care quality as well as aggravation of the situation by intention to leave service, this issue has not been properly addressed, especially among neonatal and pediatric nurses. The present study aims to identify the relationship between mental workload and musculoskeletal disorders with intention to leave the service among nurses working at neonatal and pediatric departments. METHODS: This descriptive-analytical study was conducted on 145 nurses working at neonatal and pediatric departments in six hospitals in Bushehr Province using full-census method. The data were collected using national aeronautics and space administration-task load index (NASA-TLX), Cornell musculoskeletal discomfort questionnaire(CMDQ) and Mobley and Horner's voluntary turnover questionnaire. The data were analyzed using descriptive statistics, independent t-test, Mann-Whitney U test, one-way analysis of variance (ANOVA), Kruskal-Wallis test, Pearson's and Spearman correlation tests and hierarchical linear regression in simultaneous model in SPSS 19.0. RESULTS: The mean score of intention to leave the service was 9.57 ± 3.20 (higher than the moderate level) and the mean mental workload was 71.65 ± 15.14 (high level). Pain in at least one of the legs (100%), back (77.3%) and knees (76.6%) was highly prevalent. However, no statistically significant correlation was found between musculoskeletal disorder categories and intention to leave the service (p > 0.05). The regression analysis results revealed among mental workload domains, only effort-induced workload was negatively and significantly correlated with intention to leave the service (p = 0.003; ß=-0.078). However, the number of night shifts per month was positively and significantly correlated with intention to leave the service (p = 0.001; ß = 0.176). CONCLUSIONS: Planning for appropriate allocation of night shifts, investigating the etiology of musculoskeletal disorders and providing solutions for reducing mental workload should be prioritized by policymakers, while maintaining pediatric nurses' motivation for making efforts.

Assessing workload in using electromyography (EMG)-based prostheses.

Using prosthetic devices requires a substantial cognitive workload. This study investigated classification models for assessing cognitive workload in electromyography (EMG)-based prosthetic devices with various types of input features including eye-tracking measures, task performance, and cognitive performance model (CPM) outcomes. Features selection algorithm, hyperparameter tuning with grid search, and k-fold cross-validation were applied to select the most important features and find the optimal models. Classification accuracy, the area under the receiver operation characteristic curve (AUC), precision, recall, and F1 scores were calculated to compare the models' performance. The findings suggested that task performance measures, pupillometry data, and CPM outcomes, combined with the naïve bayes (NB) and random forest (RF) algorithms, are most promising for classifying cognitive workload. The proposed algorithms can help manufacturers/clinicians predict the cognitive workload of future EMG-based prosthetic devices in early design phases.Practitioner summary: This study investigated the use of machine learning algorithms for classifying the cognitive workload of prosthetic devices. The findings suggested that the models could predict workload with high accuracy and low computational cost and could be used in assessing the usability of prosthetic devices in the early phases of the design process.Abbreviations: 3d: 3 dimensional; ADL: Activities for daily living; ANN: Artificial neural network; AUC: Area under the receiver operation characteristic curve; CC: Continuous control; CPM: Cognitive performance model; CPM-GOMS: Cognitive-Perceptual-Motor GOMS; CRT: Clothespin relocation test; CV: Cross validation; CW: Cognitive workload; DC: Direct control; DOF: Degrees of freedom; ECRL: Extensor carpi radialis longus; ED: Extensor digitorum; EEG: Electroencephalogram; EMG: Electromyography; FCR: Flexor carpi radialis; FD: Flexor digitorum; GOMS: Goals, Operations, Methods, and Selection Rules; LDA: Linear discriminant analysis; MAV: Mean absolute value; MCP: Metacarpophalangeal; ML: Machine learning; NASA-TLX: NASA task load index; NB: Naïve Bayes; PCPS: Percent change in pupil size; PPT: Purdue Pegboard Test; PR: Pattern recognition; PROS-TLX: Prosthesis task load index; RF: Random forest; RFE: Recursive feature selection; SHAP: Southampton hand assessment protocol; SFS: Sequential feature selection; SVC: Support vector classifier.

Swimming with a head-mounted display: dual-task costs.

Head-up displays (HUDs) have the potential to change work in operation environments by providing hands-free information to wearers. However, these benefits may be accompanied by trade-offs, primarily by increasing cognitive load due to dividing attention. Previous studies have attempted to understand the trade-offs of HUD usage; however, all of which were focused on land-based tasks. A gap in understanding exists when examining HUD use in aquatic environments as immersion introduces unique environmental and physiological factors that could affect multitasking. In this study, we investigated multitasking performance associated with swimming with a HUD. Eighteen participants completed three tasks: swimming only, a HUD-administered word recall task, and a dual-task combining both tasks. Results revealed significant dual-task interference in both tasks, though possibly less pronounced than in land-based tasks. These findings enhance not only help characterise dual-task performance, but also offer valuable insights for HUD design for aquatic settings.

HUDs have become an increasingly popular tool to present information to users in complex working environments. However, past research examining HUD task performance has been restricted to land-based contexts. The current study examines HUD use while swimming and provides characterisation of multitasking performance within aquatic environments.

Recognition and evaluation of mental workload in different stages of perceptual and cognitive information processing using a multimodal approach.

This study explores the effects of different perceptual and cognitive information processing stages on mental workload by assessing multimodal indicators of mental workload such as the NASA-TLX, task performance, ERPs and eye movements. Repeated measures ANOVA of the data showed that among ERP indicators, P1, N1 and N2 amplitudes were sensitive to perceptual load (P-load), P3 amplitude was sensitive to P-load only in the prefrontal region during high cognitive load (C-load) states, and P3 amplitude in the occipital and parietal regions was sensitive to C-load. Among the eye movement indicators, blink frequency was sensitive to P-load in all C-load states, but to C-load in only low P-load states; pupil diameter and blink duration were sensitive to both P-load and C-load. Based on the above indicators, the k-nearest neighbours (KNN) algorithm was used to propose a classification method for the four different mental workload states with an accuracy of 97.89%.Practitioner summary: Based on the results of this study, it is possible to implement the monitoring of mental workload states and optimise brain task allocation in operations involving high mental workload, such as human-computer interaction.

How flight experience impacts pilots' decision-making and visual scanning pattern in low-visibility approaches: preliminary evidence from eye tracking.

The visual approach is the most accident-prone phase of a flight, especially in low-visibility conditions. This preliminary study aimed to examine the effects of flight experience on pilots' decision-making and visual scanning pattern in low-visibility approaches. Twenty pilots were separated into two groups based on their flight experience and completed the high- and low-visibility approaches in balanced order using a high-fidelity flight simulator. Pilots' mental workload and visual scanning patterns were recorded via an eye tracker. The results showed that, compared to less flight-experienced pilots (20%, 3/15), experienced pilots (80%, 4/5) were more likely to make go-around decisions in the low-visibility approaches. Furthermore, they exhibited a more flexible and adaptable visual scanning pattern by quickly shifting their attention, as evidenced by decreased fixations and increased saccades. These findings suggest that the integration of visual scanning strategy and training solution with a marginally meteorological approach may enhance decision-making safety for novice pilots.

This study investigates the 'expertise effects' in visual scanning pattern, mental workload and decision-making among pilots with different levels of flight experience in a modern flight simulator. For safer decision-making, less flight-experienced pilots should enlarge their visual scanning span in a more unburdened manner, especially in low-visibility approaches.

Utilising raw psycho-physiological data and functional data analysis for estimating mental workload in human drivers.

Recent studies have focused on accurately estimating mental workload using machine learning algorithms and extracting features from physiological measures. However, feature extraction leads to the loss of valuable information and often results in binary classifications that lack specificity in the identification of optimum mental workload. This study investigates the feasibility of using raw physiological data (EEG, facial EMG, ECG, EDA, pupillometry) combined with Functional Data Analysis (FDA) to estimate the mental workload of human drivers. A driving scenario with five tasks was employed, and subjective ratings were collected. Results demonstrate that the FDA applied nine different combinations of raw physiological signals achieving a maximum 90% accuracy, outperforming extracted features by 73%. This study shows that the mental workload of human drivers can be accurately estimated without utilising burdensome feature extraction. The approach proposed in this study offers promise for mental workload assessment in real-world applications.

This study aimed to estimate the mental workload of human drivers using physiological signals and Functional Data Analysis (FDA). By comparing models using raw data and extracted features, the results show that the FDA with raw data achieved a high accuracy of 90%, outperforming the model with extracted features (73%).

The mediating effect of coping styles between emergency capacity and mental workload among clinical nurses: A cross-sectional study.

AIMS: This study aimed to examine the relationship between emergency capacity, coping styles, and mental workload among nurses. BACKGROUND: Emergency capacity, coping styles, and mental workload are all variables associated with work. Identifying the relationship between these variables can facilitate administrators to implement tailored and effective intervention strategies to improve individual performance, quality of care, and medical safety. METHODS: A quantitative cross-sectional study was carried out to investigate 605 Chinese clinical nurses in seven tertiary hospitals by using personal information form, emergency capacity scale for nurses, simplified coping skill questionnaire, and the NASA-Task Load Index. RESULTS: Emergency capacity and mental workload were found at moderate levels. The multiple linear regression model suggested that spinsterhood, no children, high workload, always anxiety or nervousness, and lower monthly income were the influencing factors of mental workload. Positive coping style was positively correlated with emergency capacity and negatively correlated with mental workload. Negative coping style was negatively related to emergency capacity and positively related to mental workload. Additionally, coping styles played a partial mediating role in the relationship between emergency capacity and mental workload through constructing a structural equation model, but the effects of positive coping style and negative coping style are opposite. CONCLUSION: Our results showed that coping styles played a mediating role in the relationship between emergency capacity and mental workload. Managers can alleviate the mental workload of nurses by cultivating positive coping styles and improving emergency capacity. IMPLICATIONS FOR NURSING AND NURSING POLICY: Mental workload of nurses deserves more attention in medical institutions. The results of our study provide evidence for improving employee health, promoting positive behaviors, and optimizing organizational management. Nursing managers should take feasible measures to fulfill nurses' needs for emergency capacity and coping strategies to alleviate nurses' mental workload, so as to stimulate their intrinsic motivation and positive organizational behavior.

A mental workload and biomechanical assessment during split-belt locomotor adaptation with and without optic flow.

Adaptive human performance relies on the central nervous system to regulate the engagement of cognitive-motor resources as task demands vary. Despite numerous studies which employed a split-belt induced perturbation to examine biomechanical outcomes during locomotor adaptation, none concurrently examined the cerebral cortical dynamics to assess changes in mental workload. Additionally, while prior work suggests that optic flow provides critical information for walking regulation, a few studies have manipulated visual inputs during adaption to split-belt walking. This study aimed to examine the concurrent modulation of gait and Electroencephalography (EEG) cortical dynamics underlying mental workload during split-belt locomotor adaptation, with and without optic flow. Thirteen uninjured participants with minimal inherent walking asymmetries at baseline underwent adaptation, while temporal-spatial gait and EEG spectral metrics were recorded. The results revealed a reduction in step length and time asymmetry from early to late adaptation, accompanied by an elevated frontal and temporal theta power; the former being well corelated to biomechanical changes. While the absence of optic flow during adaptation did not affect temporal-spatial gait metrics, it led to an increase of theta and low-alpha power. Thus, as individuals adapt their locomotor patterns, the cognitive-motor resources underlying the encoding and consolidation processes of the procedural memory were recruited to acquire a new internal model of the perturbation. Also, when adaption occurs without optic flow, a further reduction of arousal is accompanied with an elevation of attentional engagement due to enhanced neurocognitive resources likely to maintain adaptive walking patterns.

Determining the effect of selected mental factors on turnover intention through two modulators - stress and resilience over COVID-19 period.

INTRODUCTION: Turnover intention among nurses has risen in an alarming rate since the onset of the pandemic. There are various underlying factors to turnover intention. The present study aims to determine the effect of a number of mental factors on nurses' professional-turnover intention through two modulators of stress and resilience over COVID-19 period. METHODS: The current cross-sectional study was conducted at three hospitals in Khuzestan Province, southern Iran, during the winter of 2021. To collect the data, given the restrictions in place during COVID-19 period, the web link of electronic self-reported questionnaires (including general health, mental workload, work-family conflict, resilience, job stress, corona fear, and turnover intention) were sent to 350 nurses through e-mail and other social media (WhatsApp and Telegram). Accordingly, they were asked to complete the questionnaire during rest periods within two weeks. Totally, 300 people (85% participation) filled out the questionnaires. Finally, a model was constructed in the Amos software. RESULTS: The results showed that the four independent parameters of decreasing general health, increasing mental workload, increasing WFCs and fear of COVID-19 can indirectly increase nurses' turnover intention by increasing job stress. Among these variables, the highest indirect effect coefficient on turnover intention was related to the general health parameter (-0.141). The results also demonstrated a negative correlation between job stress and resilience, with lower resilience raising job stress and, consequently, increasing intention to quit the job. CONCLUSION: Mental factors affecting turnover intension were identified in this study through path analysis. Therefore, it is recommended that the required resilience-enhancing measures to be taken by hospitals and nursing administrations to reduce psychological pressures caused by mentioned variables with the aim of minimizing job-related stress and fostering nurse retention.

A Systematic Review of In-Vehicle Physiological Indices and Sensor Technology for Driver Mental Workload Monitoring.

The concept of vehicle automation ceases to seem futuristic with the current advancement of the automotive industry. With the introduction of conditional automated vehicles, drivers are no longer expected to focus only on driving activities but are still required to stay alert to resume control. However, fluctuations in driving demands are known to alter the driver's mental workload (MWL), which might affect the driver's vehicle take-over capabilities. Driver mental workload can be specified as the driver's capacity for information processing for task performance. This paper summarizes the literature that relates to analysing driver mental workload through various in-vehicle physiological sensors focusing on cardiovascular and respiratory measures. The review highlights the type of study, hardware, method of analysis, test variable, and results of studies that have used physiological indices for MWL analysis in the automotive context.

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines.

BACKGROUND AND OBJECTIVE: Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs). METHODS: Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon's task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis. RESULTS: MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively). CONCLUSIONS: The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.