Levels Of Mental Workload Research Articles

Estimating cognitive workload using a commercial in-ear EEG headset.

This study investigated the potential of estimating various mental workload levels during two different tasks using a commercial in-ear electroencephalography (EEG) system, the IDUN "Guardian".
Approach: Participants performed versions of two classical workload tasks: an n-back task and a mental arithmetic task. Both in-ear and conventional EEG data were simultaneously collected during these tasks. In an effort to facilitate a more comprehensive comparison, the complexity of the tasks was intentionally increased beyond typical levels. Special emphasis was also placed on understanding the significance of gamma band activity in workload estimations. Therefore, each signal was analyzed across low frequency (1-35 Hz) and high frequency (1-100 Hz) ranges. Additionally, surrogate in-ear EEG measures, derived from the conventional EEG recordings, were extracted and examined.
Main results: Workload estimation using in-ear EEG yielded statistically significant performance levels, surpassing chance levels with 44.1% for four classes and 68.4% for two classes in the n-back task and was better than a naive predictor for the mental arithmetic task. Conventional EEG exhibited significantly higher performance compared to in-ear EEG, achieving 80.3% and 92.9% accuracy for the respective tasks, along with lower error rates than the naive predictor. The developed surrogate measures achieved improved results, reaching accuracies of 57.5% and 85.5%, thus providing insights for enhancing future in-ear systems. Notably, most high frequency range signals outperformed their low frequency counterparts in terms of accuracy validating that high frequency gamma band features can improve workload estimation.
 Significance: The application of EEG-based Brain-Computer Interfaces (BCIs) beyond laboratory settings is often hindered by practical limitations. In-ear EEG systems offer a promising solution to this problem, potentially enabling everyday use. This study evaluates the performance of a commercial in-ear headset and provides guidelines for increased effectiveness.

Relationship Between Work Stress Level and Mental Workload to the Performance of Special School (SLB) Teachers Prof. Dr. Sri Soedewi Masjchun Sofwan, S.H. in Jambi City

The success of teachers in carrying out their duties is measured through performance. The higher the performance given, it means that the teacher is successful during the teaching process. This study aims to determine the relationship between work stress levels and mental workload with teacher performance. Cross-sectional design. The study was conducted at SLB Prof. Dr. Sri Soedewi Masjchun Sofwan, S.H. Jambi City from November to July 2024. The sampling technique was through Total Sampling with a sample size of 63 teachers. The questionnaires used were DASS-42 (work stress), Nasa-TLX (mental workload) and teacher performance questionnaires. Data analysis using the chi-square test (SPSS version 23). Based on the results of the study, it showed that 42.9% of teachers experienced moderate stress, 54.0% of teachers had high mental workload and the average teacher had good performance of 34.9%. The results of the bivariate analysis showed that the variable of work stress level with the category of experiencing stress was 24 (75.0) values (p = 0.000) PR value (4.650, 95% CI: 2.033 - 10.635), mental workload with a heavy category was 24 (70.6%) values (p = 0.000) PR value (4.094, 95% CI: 1.792 - 9.355). There is a relationship between the level of work stress and mental workload with teacher performance

Mental workload as a predictor of burnout in intensive care nurses.

Nurses face a high level of mental workload while performing their duties. High levels of mental workload can negatively impact nurses. The aim of this research was to determine the relationship between mental workload and burnout levels of intensive care nurses. Descriptive-correlational design has been adopted in this research. The sample consisted of 156 nurses working in the intensive care units of a hospital in the south of Turkey between September and December 2023. Research data were collected with the 'Participant Information Form', which includes socio-demographic characteristics, 'Mental Workload Scale' and 'Maslach Burnout Scale'. Almost all (95.5%) of the nurses participating in the study reported that their mental workload was high. It has been determined that there is a strong positive relationship between mental workload and burnout. According to the regression model obtained from the research, being a woman (β = .165, p = .008) and emotional workload (β = -695, p < .001) are determinants of emotional exhaustion. These variables explain emotional exhaustion at a rate of 50.1% (adjusted R2 = .501). Emotional workload (β = .399, p = .003) is the most important determinant of personal accomplishment. This variable explains 19.0% (adjusted R2 = .190) of personal accomplishment. The most important determinant of depersonalization is emotional workload (β = .324, p < .001). It was determined that this variable explained 24.4% (adjusted R2 = .244) of depersonalization. This research revealed that there is a strong positive relationship between mental workload and burnout and that increasing mental workload can increase burnout. Nurses' risk of emotional exhaustion increases as mental workload increases. Reducing cognitive workload, temporary workload and emotional workload increases the personal accomplishment experienced by nurses. In addition, as temporary workload, emotional workload and performance-related workload increase, the depersonalization experienced by nurses also increases.

Pengukuran Beban Kerja Mental Mahasiswa Pekerja Fakultas Ekonomi Dan Bisnis Universitas Muhammadiyah Gresik Menggunakan Metode Nasa-TLX

Background – Mental workload is the difference between the workload demands of a task and capacity. Mental workload is a human evaluation or assessment of the limits of attentional load while carrying out tasks optimally, which is influenced by the workload demands of a task and the worker's capacity to handle the task, especially for students who carry out work activities while studying. Therefore, it is necessary to examine how much mental workload is actually borne by student workers so that the negative impacts it causes can be minimized. Aim – To measure the mental workload of student workers at the Faculty of Economics and Business, Muhammadiyah University of Gresik using the NASA-TLX method Design / methodology / approach – The research approach used is Quantitative Descriptive. Quantitative data was obtained from the results of questionnaire responses from student workers at the Faculty of Economics and Business, Muhammadiyah University of Gresik regarding the level of mental workload using the NASA-TLX method. Findings – High mental workload can have a negative impact on a person's mental health, especially for students who work while studying. Mental workload is defined as the difference between task demands and an individual's capacity to handle the task, focusing more on the mental aspect than the physical. Students who work while studying often experience fatigue, lack of time to rest and socialize, and experience sleep disorders and stress which can reduce their academic and work performance. Therefore, it is important to measure and analyze the level of mental workload experienced by student workers in order to reduce its negative impact. The method used to measure mental workload is NASA-TLX (Task Load Index). Conclusion – Student workers at the Faculty of Economics and Business, Muhammadiyah University of Gresik experience a high mental workload, with the time demand indicator (Temporal Demand/TD) having the highest weight and the level of effort (Effort/EF) getting the highest score. The time requirement (TD) indicator most dominantly influences their mental workload, while the level of frustration (FR) has the lowest influence. Overall, their average mental workload score was in the high category, namely 77.64. Research implication – This research is aimed at students who are studying while working, with the aim of measuring the level of mental load that objects experience. The load in question is a high mental workload, with indicators of time requirements (Temporal Demand/TD), level of effort (Effort/EF) and indicators of time requirements (TD), as well as levels of frustration (Frustration/FR). Limitations – The limitations of this research only include the mental workload variable on the research object (student workers)

Study on comprehensive evaluation method of mental workload level

Abstract Mental workload has an important impact on the human performance of ship operators. The purpose of this study is to develop an evaluation method to evaluate the ship operator’s mental workload. First, an evaluation index system was constructed, and the DEMATEL (decision-making trial and evaluation laboratory) method was used to determine the relative weight of each index. Then, the fuzzy evaluation method was used to calculate the mental workload of ship operator based on the evaluation value and relative weight of indexes. The evaluation result of the developed method showed that the ship operators’ mental workload were 3.78 and 2.99 under tasks 1 and 2, and the ship operators’ mental workloads were at the ‘very high’ and ‘high’ levels based on the maximum membership principle. Also, the evaluation result of the developed method was consistent with the evaluation results of subjective mental workload assessment technique. It proves that the developed method can effectively evaluate the ship operator’s mental workload level.

MATB for assessing different mental workload levels.

Multi-Attribute Task Battery (MATB) is a computerized flight simulator for aviation-related tasks, suitable for non-pilots and available in many versions, including open source. MATB requires the individual or simultaneous execution of 4 sub-tasks: system monitoring (SYSMON), tracking (TRACK), communications (COMM), and resource management (RESMAN). Fully customizable, the design of test duration, number of sub-tasks used, event rates, response times and overlap, create different levels of mental load. MATB can be combined with an additional auditory attention (Oddball) task, or with physiological constraints (i.e., sleep loss, exercise, hypoxia). We aimed to assess the main characteristics of MATB design for assessing the response to different workload levels. We identified and reviewed 19 articles for which the effects of low and high workload were analyzed. Although MATB has shown promise in detecting performance degradation due to increase workload, studies have yielded conflicting or unclear results regarding MATB configurations. Increased event rates, number of sub-tasks (multitasking), and overlap are associated with increased perceived workload score (ex. NASA-TLX), decreased performance (especially tracking), and neurophysiological responses, while no effect of time-on-task is observed. The median duration used for the test is 20min (range 12-60) with a level duration of 10min (range 4-15). To assess mental workload, the median number of stimuli is respectively 3 events/min (range 0.6-17.2) for low, and 23.5 events/min (range 9-65) for high workload level. In this review, we give some recommendations for standardization of MATB design, configuration, description and training, in order to improve reproducibility and comparison between studies, a challenge for the future researches, as human-machine interaction and digital influx increase for pilots. We also open the discussion on the possible use of MATB in the context of aeronautical/operational constraints in order to assess the effects combined with changes in mental workload levels. Thus, with appropriate levels of difficulty, MATB can be used as a suitable simulation tool to study the effects of changes on the mental workload of aircraft pilots, during different operational and physiological constraints.

Combined Effects of Moderate Hypoxia and Sleep Restriction on Mental Workload.

Aircraft pilots face a high mental workload (MW) under environmental constraints induced by high altitude and sometimes sleep restriction (SR). Our aim was to assess the combined effects of hypoxia and sleep restriction on cognitive and physiological responses to different MW levels using the Multi-Attribute Test Battery (MATB)-II with an additional auditory Oddball-like task. Seventeen healthy subjects were subjected in random order to three 12-min periods of increased MW level (low, medium, and high): sleep restriction (SR, <3 h of total sleep time (TST)) vs. habitual sleep (HS, >6 h TST), hypoxia (HY, 2 h, FIO2 = 13.6%, ~3500 m vs. normoxia, NO, FIO2 = 21%). Following each MW level, participants completed the NASA-TLX subjective MW scale. Increasing MW decreases performance on the MATB-II Tracking task (p = 0.001, MW difficulty main effect) and increases NASA-TLX (p = 0.001). In the combined HY/SR condition, MATB-II performance was lower, and the NASA-TLX score was higher compared with the NO/HS condition, while no effect of hypoxia alone was observed. In the accuracy of the auditory task, there is a significant interaction between hypoxia and MW difficulty (F(2-176) = 3.14, p = 0.04), with lower values at high MW under hypoxic conditions. Breathing rate, pupil size, and amplitude of pupil dilation response (PDR) to auditory stimuli are associated with increased MW. These parameters are the best predictors of increased MW, independently of physiological constraints. Adding ECG, SpO2, or electrodermal conductance does not improve model performance. In conclusion, hypoxia and sleep restriction have an additive effect on MW. Physiological and electrophysiological responses must be taken into account when designing a MW predictive model and cross-validation.

Work-Related Psychosocial Factors and Their Effects on Mental Workload Perception and Body Postures.

The perception of work is closely linked to body reactions that facilitate task performance. Previous studies have shown that psychosocial work factors significantly impact employee health on both psychological and physical levels, though their cross-sectional designs limit causal interpretations. In this study, participants performed sitting and standing tasks under four different levels of mental workload. The NASA-Task Load Index (NASA-TLX) assessed mental workload perception across six dimensions, while Rapid Entire Body Assessment (REBA) and Rapid Upper Limb Assessment (RULA) scores evaluated body postures for standing and sitting tasks, respectively. This study examined the effects of alarms, distractions, and time constraints-common psychosocial factors in healthcare environments-on human behavior. We compared NASA-TLX scores with corresponding REBA/RULA scores to evaluate how perceived mental workload affects body postures. One-way ANOVA assessed the impact of experimental conditions on response variables, and Pearson correlation analyses explored the relationships between psychosocial factors and these variables. Results indicated that alarm conditions most negatively impacted mental workload perception and body postures. Temporal demand and effort scores were particularly affected by psychosocial factors in both tasks. Gender influenced physical demand and performance scores (higher in females) for the standing task but did not affect REBA and RULA scores. These findings suggest that organizational and psychosocial factors significantly influence healthcare workers' behavior, health, and patient safety. Further research is needed to evaluate the specific effects of psychosocial factors on both physical and mental workload to understand the relationship between overall task workload and occupational disorders.

Study of mental workload in public administration managers

The present research is carried out in the Municipal Administration with the objective of studying the behavior of the mental workload in the work places. The procedure used consists of three stages: preparation, experiments and results. The indicators selected for the study are: Simple Reaction Time, Complex Reaction Time, Discrimination Threshold, Depth Perception and Yoshitake Test are selected. SPSS software is used or the statistical analysis of the data, EndNote as a bibliographic manager and VOSviewer for its graphical representation. An Ishikawa Diagram is used to determine the causes that provoke mental fatigue during the working day in the individuals studied. As a result, out of the 12 workers studied, 6 presented extreme risk, 5 a worrying level and 1 a moderate level of mental workload. A proposal of measures to prevent and reduce the effect of mental workload is elaborated.

The knowledge, attitudes, and practices of arteriovenous access assessment among hemodialysis nurses: A multicenter cross-sectional survey.

Hemodialysis nurses' cannulation technique, monitoring, and management methods can affect patients' vascular access longevity. An arteriovenous access assessment comprises a medical history and vascular assessment through physical examination and surveillance. However, further exploration is needed to fully understand hemodialysis nurses' knowledge, attitudes, and practices (KAP) in this area. Between June and July 2023, we recruited hemodialysis nurses from 21 cities in Guangdong Province using a convenience sampling method. Four questionnaires-the General Data Questionnaire, the KAP Scale of Arteriovenous Access Assessment among Hemodialysis Nurses, the Utrecht Work Engagement Scale, and the NASA Task Load Index-were utilized for data collection through the Questionnaire Star platform. Of the 530 hemodialysis nurses participating in the study, 458 (86.4%) had a valid response. The participants demonstrated moderate knowledge and practice levels regarding arteriovenous access assessment and exhibited positive attitudes. We identified several factors related to arteriovenous access assessment that predict KAP in hemodialysis nurses. These factors included years of experience as a hemodialysis nurse, whether a nurse's knowledge of physical examination was sufficient to meet clinical needs, whether a nurse had received training in performing physical examination, whether a nurse's department regularly checked the quality of physical examination, and nurses' levels of work engagement and mental workload. All factors explained 32.4% of the variance in participants' KAP regarding arteriovenous access assessment. Improving hemodialysis nurses' assessment of arteriovenous access is crucial to ensure optimal patient care. Dialysis center managers and educators should prioritize understanding hemodialysis nurses' KAP of arteriovenous access assessment and any factors influencing these areas.

Paramedic Mental Workload in Pandemic Covid-19 Era

The number of Covid-19 patients hospitalized continues to increase in line with the increase in Covid-19 cases in Indonesia. The increasing number of Covid-19 patients in hospitals is increasing the workload of paramedics. High workloads will cause saturation and fatigue. In addition, an increase in the number of Covid-19 patients increases anxiety and depression will be infected when treating patients. Paramedics should also use personal protective equipment during shift guards which causes dehydration and discomfort. This situation can degrade the quality of service to patients. The study aims to measure the mental workload of paramedics by NASA-TLX (National Aeronautics and Space Administration Task Load Index) method and find out the factors that affect mental workload levels. This research is a quantitative research with a cross sectional approach. The research was conducted at Hospital X type C in Malang Regency in October 2020. The samples used were all 100 Hospital X paramedics. Research instruments in the form of questionnaires shared through Google Form. Data analysis is done with descriptive analysis and Chi-Square analysis with p value &lt;0.05. The results found paramedics had a low mental workload of 1%, a moderate mental load of 3%, a high mental workload of 47%, and a very high mental workload of 49%. The Chi-Square test results found that age factors had an effect on the mental workload level of paramedics.

Association of auditory processing abilities and employment in young women

BackgroundThis study aimed to investigate the relationship between employment status and auditory processing abilities in women, considering the cognitive impact of work-related demands. The objective was to study the influence of employment on forward -, backward -, operation span, 2n back, and speech perception in noise (SPIN) scores.MethodsFifty-eight women aged 20 to 64 years were categorized into working and non-working groups. Auditory cognitive assessments included forward and backward digit span, auditory 2n-back, operational span, and speech perception in noise. Mental workload, listening effort, and fatigue levels were evaluated for each test using standardized scales. Handedness was assessed using the Edinburgh Handedness Inventory. Statistical analysis involved Mann–Whitney tests and effect size calculations.ResultsWorking women scored significantly higher in 2n-back task compared to non-working counterparts, emphasizing the positive impact of employment on working memory and cognitive control processes. The working women group perceptually rated greater mental load on the operational span task than non-working group, indicating the task-specific manifestation of group differences. When the tasks are easier (2n back) or difficult (operation span), employed women exhibited an advantage in auditory processing over their counterparts. ConclusionThis study reveals nuanced cognitive differences influenced by employment status in women. Working women demonstrated superior auditory processing abilities, specifically in working memory tasks, suggesting potential cognitive benefits associated with engagement in the workforce. The elevated mental workload for dual task working memory paradigms underscores the complexity of simultaneous processing and storage, highlighting cognitive challenges in specific occupational contexts.

ECG and EEG based machine learning models for the classification of mental workload and stress levels for women in different menstrual phases, men, and mixed sexes

ECG and EEG based machine learning models for the classification of mental workload and stress levels for women in different menstrual phases, men, and mixed sexes

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

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. A cross-sectional questionnaire study. 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. 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. 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. 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. No patient or public contribution.

Classification of mental workload using brain connectivity and machine learning on electroencephalogram data

Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36).

Mental workload estimation with electroencephalogram signals by combining multi-space deep models

The human brain is perpetually active, operating during both work and rest. Excessive mental activity, termed overload, can detrimentally impact health. Advances in predicting mental health conditions aim to prevent severe consequences and improve overall well-being. Consequently, mental status estimation has gained considerable research attention due to its potential benefits. Among the various signals used to assess mental state, the electroencephalogram, with its wealth of brain-related information, is widely employed by researchers. In this paper, we categorize mental workload into three states (low, middle, and high) and estimate a continuum of mental workload levels. Our method leverages information from multiple spatial dimensions (time and frequency domain) to achieve optimal results. In our approach, focusing on the time domain, we utilize Temporal Convolutional Networks to capture temporal information. Meanwhile, in the frequency domain, we implement two distinct branches aimed at acquiring varied information regarding Power Spectral Density by utilizing different lengths of the Fast Fourier Transform. Each branch consists of two blocks, known as the Multi-Dimensional Residual Block, representing a novel architecture by combining residual blocks. The integration of these two domains yields significant results compared to individual estimates in each domain. Our approach achieved a 74.98% accuracy in the three-class classification, surpassing the provided data results at 69.00%. Specially, our method demonstrates efficacy in estimating continuous levels, evidenced by a corresponding Concordance Correlation Coefficient result of 0.629. The combination of time and frequency domain analysis in our approach highlights the exciting potential to improve healthcare applications in the future.

Assessment of Mental Workload Level Based on PPG Signal Fusion Continuous Wavelet Transform and Cardiopulmonary Coupling Technology

Mental workload is an important predisposing factor for mental illnesses such as depression and is closely related to individual mental health. However, the suboptimal accuracy of utilizing photoplethysmography (PPG) exclusively for mental workload classification has constrained its application within pertinent professional domains. To this end, this paper proposes a signal processing method that combines continuous wavelet transform (CWT) and cardiopulmonary coupling mapping (CPC) to classify mental load via a convolutional neural network (ResAttNet). The method reflects changes in mental workload, as assessed by changes in the association between heart rate variability and respiration. In this paper, the strengths and weaknesses of this method are compared with other traditional psychological workload monitoring methods, such as heart rate variability (HRV), and its validation is performed on the publicly available dataset MAUS. The experiments show that the method is significantly better than previous machine learning methods based on heart rate variability correlation. Meanwhile, the accuracy of the method proposed in this paper reaches 80.5%, which is 6.2% higher than in previous studies. It is comparable to the result of 82.4% for the ECG-based mental workload monitoring system. Therefore, the method of combining CWT and CPC has considerable potential and provides new ideas for mental workload classification.

Effect Of Mental Workload And Moral Sensitivity On Nurse Performance In The Acute Inpatient Room Of The Prof. Dr. M. Ildrem Mental Hospital, Medan

Objectives: High mental workload can decrease nurse performance. Nurses with higher moral sensitivity are ethically more practicing professionals and the quality of care is better. This study assesses effect of Mental Workload and Moral Sensitivity on Nurse Performance in the Acute Inpatient Room of the Prof. Dr. M. Idrelm Mental Hospital Medan in Indonesia . Methods: The study used a cross-sectional design, sampel number of 58 nurses in acutel inpatient room through total sampling. NASA-TLX Questionnaires for Mental Workload, Moral Sensitivity Questionnaires for Moral Sensitivity and Individual Work Performance Questionnaires for Nurse Performance. Analyzed data using the Statistical Package for social Science (SPSS) version 26 software. Descriptive statistics, Pearson test and multiple linear regression analysis to measure its effect. A p-vaule of &lt; 0.05 was considered statistically significant at a 95% confidence level. Result: Majority of participant had a high level of mental workload (86.2%), high level of moral sensitivity (58.6%) and had good level of performance (53.4%). Further, there is no correlation mental workload with nurses performance (p = 0.350), but moral sensitivity significantly correlated with nurses performance (p = 0.041). Conclusion: Nurse has level mental workload high indicate prone to fatique and burnout, but they have good performance because they have adapted to the existing work situation. This is supported by high moral sensitivity so that nurses feel very responsible for their profession.

THE ROLE OF MENTAL WORKLOAD IN INVESTING QUALITY OF LIFE AMONG WORKERS IN THE PHOSPHORIC ACID INDUSTRY

Introduction: Every workplace has potential hazards, one of which is psychological hazards, namely work stress. The stress felt by workers can interfere with their work activities if not controlled immediately. In Indonesia, 11.6–17.3% of the 150 million adult population suffers from stress at work. Moreover, job stress can be caused by various factors. Aims: To assess workers’ quality of work life by analyzing the relationship between mental workload and work stress. Moreover, this research investigates the individual characteristics, mental workload level, and stress level between workers. Methods: This research is a quantitative study using an observational design and a cross-sectional technique. The participants in this study were all 35 maintenance personnel. Mental workload and work stress are assessed using NASA-TLX and DASS-21 questionnaire, respectively. Results: The collected data are statistically analyzed using the Spearman correlation test and the Cramer contingency coefficient correlation test. The results showed that mental workload has a strong relationship with work stress which is in the strong category (r= 0,679) and the direction of the relationship is unidirectional. Conclusion: The conclusion of this study is that the higher the mental workload possessed by workers, the higher the level of perceived work stress. Therefore, the company can reduce work stress by conducting both individual and organizational strategies

Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data

The main contribution of this study was the concurrent application of EEG and eye tracking techniques during n-back tasks as part of the methodology for addressing the problem of mental workload classification through machine learning algorithms. The experiments involved 15 university students, consisting of 7 women and 8 men. Throughout the experiments, the researchers utilized the n-back memory task and the NASA-Task Load Index (TLX) subjective rating scale to assess various levels of mental workload. The results indicating the relationship between EEG and eye tracking measures and mental workload are consistent with previous research. Regarding the four-class classification task, mental workload level could be predicted with 76.59% accuracy using 34 selected features. This study makes a significant contribution to the literature by presenting a four-class mental workload estimation model that utilizes different machine learning algorithms.