EEG Source Localization during an Arm Isometric Force Exertion Task at Different Levels of Perceived Exertion.

BACKGROUND: Neuroergonomics is an emerging science that focuses on the human brain's performance during physical work. The advent of portable neurophysiological methods, including electroencephalography (EEG), has enabled measurements of real-time brain activity during physical tasks without restricting body movements. However, the EEG signatures of different levels of physical exertion activity involving the musculoskeletal system remain poorly understood. OBJECTIVE: This study investigated the EEG source localization activity induced by predefined force exertion levels during an isometric arm force exertion task in healthy female participants for the alpha and beta frequency bands. METHODS: Exact low-resolution electromagnetic tomography (eLORETA) was used to localize the current source densities (CSDs) in 84 anatomical brain regions of interest. RESULTS: The maximum CSDs for extremely hard force exertion levels for the alpha frequency were localized in Brodmann area (BA) 6, whereas CSDs associated with other exertion levels were localized in BA 8. The maximum CSDs for extremely hard force exertion levels for beta were localized in BA 5, whereas CSDs associated with other exertion levels were localized in BA 7. CONCLUSIONS: These findings extend the current understanding of the neurophysiological basis of physical exertion with various force levels and suggest that specific brain regions are involved in generating the sensation of force exertion. To our knowledge, this is the first study localizing EEG activity among various predefined force exertion levels during an isometric arm exertion task in healthy female participants.

Electroencephalography (EEG) Physiological Indices Reflecting Human Physical Performance: A Systematic Review Using Updated PRISMA.

BACKGROUND: With the advent of portable neurophysiological methods, including electroencephalography, progress in studying brain activity during physical tasks has received considerable attention, predominantly in clinical exercise and sports studies. However, the neural signatures of physical tasks in everyday settings were less addressed. METHODS: Electroencephalography (EEG) indices are sensitive to fluctuations in the human brain, reflecting spontaneous brain activity with an excellent temporal resolution. OBJECTIVE: In this regard, this study attempts to systematically review the feasibility of using EEG indices to quantify human performance in various physical activities in both laboratory and real-world applications. A secondary goal was to examine the feasibility of using EEG indices for quantifying human performance during physical activities with mental tasks. The systematic review was conducted based on the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: Out of 81 studies, 64 task studies focused on quantifying human performance concerning physical activity, whereas 17 studies focused on quantifying human performance on physical activities associated with mental tasks. EEG studies have primarily relied on linear methods, including the power spectrum, followed by the amplitude of Event-related potential components, to evaluate human physical performance. The nonlinear methods were relatively less addressed in the literature. Most studies focused on assessing the brain activity associated with muscular fatigue tasks. The upper anatomical areas have been discussed in several occupational schemes. The studies addressing biomechanical loading on the torso and spine, which are the risk factors for musculoskeletal disorders, are less addressed. CONCLUSIONS: Despite the recent interest in investigating the neural mechanisms underlying human motor functioning, assessing the brain signatures of physical tasks performed in naturalistic settings is still limited.

Diurnal variations of resting-state fMRI data: A graph-based analysis.

Circadian rhythms (lasting approximately 24 h) control and entrain various physiological processes, ranging from neural activity and hormone secretion to sleep cycles and eating habits. Several studies have shown that time of day (TOD) is associated with human cognition and brain functions. In this study, utilizing a chronotype-based paradigm, we applied a graph theory approach on resting-state functional MRI (rs-fMRI) data to compare whole-brain functional network topology between morning and evening sessions and between morning-type (MT) and evening-type (ET) participants. Sixty-two individuals (31 MT and 31 ET) underwent two fMRI sessions, approximately 1 hour (morning) and 10 h (evening) after their wake-up time, according to their declared habitual sleep-wake pattern on a regular working day. In the global analysis, the findings revealed the effect of TOD on functional connectivity (FC) patterns, including increased small-worldness, assortativity, and synchronization across the day. However, we identified no significant differences based on chronotype categories. The study of the modular structure of the brain at mesoscale showed that functional networks tended to be more integrated with one another in the evening session than in the morning session. Local/regional changes were affected by both factors (i.e., TOD and chronotype), mostly in areas associated with somatomotor, attention, frontoparietal, and default networks. Furthermore, connectivity and hub analyses revealed that the somatomotor, ventral attention, and visual networks covered the most highly connected areas in the morning and evening sessions: the latter two were more active in the morning sessions, and the first was identified as being more active in the evening. Finally, we performed a correlation analysis to determine whether global and nodal measures were associated with subjective assessments across participants. Collectively, these findings contribute to an increased understanding of diurnal fluctuations in resting brain activity and highlight the role of TOD in future studies on brain function and the design of fMRI experiments.

Evidence of Chaos in Human Emotions Expressed in Tweets.

This study explored the chaotic properties of human emotions as expressed in social media and its implications for attainable forecasting horizons. Three human emotional states extracted from Twitter were analyzed using the nonlinear dynamics approach. The greatest positive Lyapunov exponent (LE) and 0-1 test methods were applied to a time series set consisting of over 25,000 data points reflecting the hourly recorded data of over 1.3 million tweets. The results suggest that the examined emotional time series data represent a nonlinear dynamical system with deterministic chaos properties. Therefore, by utilizing traditional linear methods of social media data analysis, one may not be able to fully understand and forecast critical transition trends over time or beyond a limited duration. It was concluded that the nonlinear dynamics approach is useful to determine a feasible forecasting horizon and to assess the prediction accuracy of social media data in general.

Evidence of Chaos in a Routine Watchstanding Task.

Research into human performance on naval ships often emphasizes the significance of shaping factors, such as vigilance, fatigue and circadian rhythm. However, few report on the presence of complex nonlinear dynamics and fractal dimensionality. This study examines over 7000 routine topside security checks on a U.S. Navy Destroyer performed over a twelve-month period. Time series analysis (0-1 test, analysis of Lyapunov exponent, state space plots, and graphical analysis) reveal the presence of chaotic behavior, harmonics attractors, and fractal structures. The presence of this level of complexity in a simple task demonstrates the need to integrate previous findings in human performance with new discoveries in complexity and fractional dynamics.

A method for predicting the risk of virtual crashes in a simulated driving task using behavioural and subjective drowsiness measures.

This study proposed a procedure for predicting the point in time with high risk of virtual crash using a control chart methodology for behavioural measures during a simulated driving task. Tracking error, human back pressure, sitting pressure and horizontal and vertical neck bending angles were measured during the simulated driving task. The time with a high risk of a virtual crash occurred in 9 out of 10 participants. The time interval between the successfully detected point in time with high risk of virtual crash and the point in time of virtual crash ranged from 80 to 324 s. The proposed procedure for predicting the point in time with a high risk of a crash is promising for warning drivers of the state of high risk of crash. Practitioner Summary: Many fatal crashes occur due to drowsy driving. We proposed a method to predict the point in time with high risk of virtual crash before such a virtual crash occurs. This is done using behavioural measures during a simulated driving task. The effectiveness of the method is also demonstrated.

Detection of error-related negativity in complex visual stimuli: a new neuroergonomic arrow in the practitioner's quiver.

Brain processes responsible for the error-related negativity (ERN) evoked response potential (ERP) have historically been studied in highly controlled laboratory experiments through presentation of simple visual stimuli. The present work describes the first time the ERN has been evoked and successfully detected in visual search of complex stimuli. A letter flanker task and a motorcycle conspicuity task were presented to participants during electroencephalographic (EEG) recording. Direct visual inspection and subsequent statistical analysis of the resultant time-locked ERP data clearly indicated that the ERN was detectable in both groups. Further, the ERN pattern did not differ between groups. Such results show that the ERN can be successfully elicited and detected in visual search of complex static images, opening the door to applied neuroergonomic use. Harnessing the brain's error detection system presents significant opportunities and complex challenges, and implication of such are discussed in the context of human-machine systems. Practitioner Summary: For the first time, error-related negativity (ERN) has been successfully elicited and detected in a visually complex applied search task. Brain-process-based error detection in human-machine systems presents unique challenges, but promises broad neuroergonomic applications.

Psychophysical basis for maximum pushing and pulling forces: A review and recommendations.

The objective of this paper was to perform a comprehensive review of psychophysically determined maximum acceptable pushing and pulling forces. Factors affecting pushing and pulling forces are identified and discussed. Recent studies show a significant decrease (compared to previous studies) in maximum acceptable forces for males but not for females when pushing and pulling on a treadmill. A comparison of pushing and pulling forces measured using a high inertia cart with those measured on a treadmill shows that the pushing and pulling forces using high inertia cart are higher for males but are about the same for females. It is concluded that the recommendations of Snook and Ciriello (1991) for pushing and pulling forces are still valid and provide reasonable recommendations for ergonomics practitioners. Regression equations as a function of handle height, frequency of exertion and pushing/pulling distance are provided to estimate maximum initial and sustained forces for pushing and pulling acceptable to 75% male and female workers. At present it is not clear whether pushing or pulling should be favored. Similarly, it is not clear what handle heights would be optimal for pushing and pulling. Epidemiological studies are needed to determine relationships between psychophysically determined maximum acceptable pushing and pulling forces and risk of musculoskeletal injuries, in particular to low back and shoulders.

The identification of factors contributing to self-reported anomalies in civil aviation.

The main objective of this study was to analyze anomalies voluntarily reported by pilots in civil aviation sector and identify factors leading to such anomalies. Experimental data were obtained from the NASA aviation safety reporting system (ASRS) database. These data contained a range of text records spanning 30 years of civilian aviation, both commercial (airline operations) and general aviation (private aircraft). Narrative data as well as categorical data were used. The associations between incident contributing factors and self-reported anomalies were investigated using data mining and correspondence analysis. The results revealed that a broadly defined human factors category and weather conditions were the main contributors to self-reported civil aviation anomalies. New associations between identified factors and reported anomaly conditions were also reported.

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review.

(1) Background: Chaos, a feature of nonlinear dynamical systems, is well suited for exploring biological time series, such as heart rates, respiratory records, and particularly electroencephalograms. The primary purpose of this article is to review recent studies using chaos theory and nonlinear dynamical methods to analyze human performance in different brain processes. (2) Methods: Several studies have examined chaos theory and related analytical tools for describing brain dynamics. The present study provides an in-depth analysis of the computational methods that have been proposed to uncover brain dynamics. (3) Results: The evidence from 55 articles suggests that cognitive function is more frequently assessed than other brain functions in studies using chaos theory. The most frequently used techniques for analyzing chaos include the correlation dimension and fractal analysis. Approximate, Kolmogorov and sample entropy account for the largest proportion of entropy algorithms in the reviewed studies. (4) Conclusions: This review provides insights into the notion of the brain as a chaotic system and the successful use of nonlinear methods in neuroscience studies. Additional studies of brain dynamics would aid in improving our understanding of human cognitive performance.

Simplicity and predictability: a phenomenological study of psychological flow in transactional workers.

Psychological flow is a positive experience achieved through a near-balance of task challenge and skill capability, creating a merging of awareness and action and leading to an intrinsically rewarding feeling. Flow has typically been documented in persons who participate in work and leisure activities where they can exercise a large degree of creativity and agency over their actions in pursuit of their goals. The objective of the present study is to explore the lived experiences of flow in workers in roles where creativity and agency are typically not expected. An interpretative phenomenological analysis approach was employed to attain this objective. Semi-structured interviews were conducted with 17 adults whose role is to perform transactional work, which by its nature affords less opportunity for creative execution. Common themes about participants' flow experiences are documented. Two broad types of flow are described and a connection is made that the present study's participants achieve one of those flow types while working. Participants' feelings, preferences, and actions are mapped to the nine conventional dimensions of flow. Specific non-task work system factors are discussed relative to their influence on participants' attainment of flow. Limitations of the present study and recommended future research are discussed.

Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis.

Background: Considering that brain activity involves communication between millions of neurons in a complex network, nonlinear analysis is a viable tool for studying electroencephalography (EEG). The main objective of this review was to collate studies that utilized chaotic measures and nonlinear dynamical analysis in EEG of multiple sclerosis (MS) patients and to discuss the contributions of chaos theory techniques to understanding, diagnosing, and treating MS. Methods: Using the preferred reporting items for systematic reviews and meta-analysis (PRISMA), the databases EbscoHost, IEEE, ProQuest, PubMed, Science Direct, Web of Science, and Google Scholar were searched for publications that applied chaos theory in EEG analysis of MS patients. Results: A bibliographic analysis was performed using VOSviewer software keyword co-occurrence analysis indicated that MS was the focus of the research and that research on MS diagnosis has shifted from conventional methods, such as magnetic resonance imaging, to EEG techniques in recent years. A total of 17 studies were included in this review. Among the included articles, nine studies examined resting-state, and eight examined task-based conditions. Conclusion: Although nonlinear EEG analysis of MS is a relatively novel area of research, the findings have been demonstrated to be informative and effective. The most frequently used nonlinear dynamics analyses were fractal dimension, recurrence quantification analysis, mutual information, and coherence. Each analysis selected provided a unique assessment to fulfill the objective of this review. While considering the limitations discussed, there is a promising path forward using nonlinear analyses with MS data.

Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review.

(1) Background: Multiple sclerosis (MS) is an immune system disease in which myelin in the nervous system is affected. This abnormal immune system mechanism causes physical disabilities and cognitive impairment. Functional magnetic resonance imaging (fMRI) is a common neuroimaging technique used in studying MS. Computational methods have recently been applied for disease detection, notably graph theory, which helps researchers understand the entire brain network and functional connectivity. (2) Methods: Relevant databases were searched to identify articles published since 2000 that applied graph theory to study functional brain connectivity in patients with MS based on fMRI. (3) Results: A total of 24 articles were included in the review. In recent years, the application of graph theory in the MS field received increased attention from computational scientists. The graph-theoretical approach was frequently combined with fMRI in studies of functional brain connectivity in MS. Lower EDSSs of MS stage were the criteria for most of the studies (4) Conclusions: This review provides insights into the role of graph theory as a computational method for studying functional brain connectivity in MS. Graph theory is useful in the detection and prediction of MS and can play a significant role in identifying cognitive impairment associated with MS.

Towards better understanding of workplace factors contributing to hospitalist burden and burnout prior to COVID-19 pandemic.

BACKGROUND: Hospitalists are physicians trained in internal medicine and play a critical role in delivering care in in-patient settings. They work across and interact with a variety of sub-systems of the hospital, collaborate with various specialties, and spend their time exclusively in hospitals. Research shows that hospitalists report burnout rates above the national average for physicians and thus, it is important to understand the key factors contributing to hospitalists' burnout and identify key priorities for improving hospitalists' workplace. METHODS: Hospitalists at an academic medical center and a community hospital were recruited to complete a survey that included demographics, rating the extent to which socio-technical (S-T) factors contributed to burnout, and 22-item Maslach Burnout Inventory - Human Services Survey (MBI-HSS). Twelve contextual inquiries (CIs) involving shadowing hospitalists for ∼60 h were conducted varied by shift type, length of tenure, age, sex, and location. Using data from the survey and CIs, an affinity diagram was developed and presented during focus groups to 12 hospitalists to validate the model and prioritize improvement efforts. RESULTS: The overall survey participation rate was 68%. 76% of hospitalists reported elevated levels on at least one sub-component within the MBI. During CIs, key breakdowns were reported in relationships, communication, coordination of care, work processes in electronic healthcare records (EHR), and physical space. Using data from CIs, an affinity diagram was developed. Hospitalists voted the following as key priorities for targeted improvement: improve relationships with other care team members, improve communication systems and prevent interruptions and disruptions, facilitate coordination of care, improve workflows in EHR, and improve physical space. CONCLUSIONS: This mixed-method study utilizes participatory and data-driven approaches to provide evidence-based prioritization of key factors contributing to hospitalists' burnout. Healthcare systems may utilize this approach to identify workplace factors contributing to provider burnout and consider targeting the factors identified by providers to best optimize scarce resources.

Isometric Arm Forces Exerted by Females at Different Levels of Physical Comfort and Their EEG Signatures.

A variety of subjective measures have traditionally been used to assess the perception of physical exertion at work and related body responses. However, the current understanding of physical comfort experienced at work is very limited. The main objective of this study was first to investigate the magnitude of isometric arm forces exerted by females at different levels of physical comfort measured on a new comfort scale and, second, to assess their corresponding neural signatures expressed in terms of power spectral density (PSD). The study assessed PSDs of four major electroencephalography (EEG) frequency bands, focusing on the brain regions controlling motor and perceptual processing. The results showed statistically significant differences in exerted arm forces and the rate of perceived exertion at the various levels of comfort. Significant differences in power spectrum density at different physical comfort levels were found for the beta EEG band. Such knowledge can be useful in incorporating female users' force requirements in the design of consumer products, including tablets, laptops, and other hand-held information technology devices, as well as various industrial processes and work systems.

Nonlinear dynamics of EEG responses to unmanned vehicle visual detection with different levels of task difficulty.

The main objective of this study was to examine the presence of chaos in the EEG recordings of brain activity under simulated unmanned ground vehicle visual detection scenarios with different levels of task difficulty. One hundred and fifty people participated in the experiment and completed four visual detection task scenarios: (1) change detection, (2) a threat detection task, (3) a dual-task with different change detection task rates, and (4) a dual-task with different threat detection task rates. We used the largest Lyapunov exponent and correlation dimension of the EEG data and performed 0-1 tests on the EEG data. The results revealed a change in the level of nonlinearity in the EEG data corresponding to different levels of cognitive task difficulty. The differences in EEG nonlinearity measures among the studied levels of task difficulty, as well as between a single task scenario and a dual-task scenario, have also been assessed. The results increase our understanding of the nature of unmanned systems' operational requirements.

A whole body postural loading simulation and assessment model for workplace analysis and design.

This study reports on the development and validation of a new computer model for simulating human postures at work, and assessing the reaction forces and bending moments in 43 human articulation joints. The proposed model estimates the intradiscal pressure in the vertebral column in response to external loading forces encountered during human interactions with work objects or processes. The model was implemented in a self-contained interactive software package. The simulation results compare favorably with the reported experimental data, and provide reasonable confidence in the quality of the model. Its characteristics and its applications in evaluating physical task performance are also discussed.

Assessing Patient Safety Culture in United States Hospitals.

A positive patient safety culture plays a major role in reducing medical errors and increasing productivity among healthcare staff. Furthermore, understanding staff perceptions of patient safety culture and effective patient safety factors is a first step toward enhancing quality of care and patient safety. The objectives of this study were to assess patient safety culture in hospitals in the United States and to investigate the effects of hospital and respondent characteristics on perceived patient safety culture. An analysis of 67,010 respondents in the 2018 Agency for Healthcare Research and Quality (AHRQ) comparative database was conducted with partial least squares structural equation modeling (PLS-SEM). The results revealed that perceptions of patient safety culture had a positive influence on the overall perceptions of patient safety and frequency of event reporting. Moreover, staff position, teaching status, and geographic region were found to have varying influence on the patient safety culture, overall perceptions of patient safety, and frequency of event reporting.

Explainable AI: A review of applications to neuroimaging data.

Deep neural networks (DNNs) have transformed the field of computer vision and currently constitute some of the best models for representations learned via hierarchical processing in the human brain. In medical imaging, these models have shown human-level performance and even higher in the early diagnosis of a wide range of diseases. However, the goal is often not only to accurately predict group membership or diagnose but also to provide explanations that support the model decision in a context that a human can readily interpret. The limited transparency has hindered the adoption of DNN algorithms across many domains. Numerous explainable artificial intelligence (XAI) techniques have been developed to peer inside the "black box" and make sense of DNN models, taking somewhat divergent approaches. Here, we suggest that these methods may be considered in light of the interpretation goal, including functional or mechanistic interpretations, developing archetypal class instances, or assessing the relevance of certain features or mappings on a trained model in a post-hoc capacity. We then focus on reviewing recent applications of post-hoc relevance techniques as applied to neuroimaging data. Moreover, this article suggests a method for comparing the reliability of XAI methods, especially in deep neural networks, along with their advantages and pitfalls.

The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach.

Coronavirus disease 2019 (COVID-19) was first discovered in China; within several months, it spread worldwide and became a pandemic. Although the virus has spread throughout the globe, its effects have differed. The pandemic diffusion network dynamics (PDND) approach was proposed to better understand the spreading behavior of COVID-19 in the US and Japan. We used daily confirmed cases of COVID-19 from 5 January 2020 to 31 July 2021, for all states (prefectures) of the US and Japan. By applying the pandemic diffusion network dynamics (PDND) approach to COVID-19 time series data, we developed diffusion graphs for the US and Japan. In these graphs, nodes represent states and prefectures (regions), and edges represent connections between regions based on the synchrony of COVID-19 time series data. To compare the pandemic spreading dynamics in the US and Japan, we used graph theory metrics, which targeted the characterization of COVID-19 bedhavior that could not be explained through linear methods. These metrics included path length, global and local efficiency, clustering coefficient, assortativity, modularity, network density, and degree centrality. Application of the proposed approach resulted in the discovery of mostly minor differences between analyzed countries. In light of these findings, we focused on analyzing the reasons and defining research hypotheses that, upon addressing, could shed more light on the complex phenomena of COVID-19 virus spread and the proposed PDND methodology.