Automated Cognitive Workload Assessment Using Logical Teaching Learning-Based Optimization and PROMETHEE Multi-Criteria Decision Making Approach

We aimed to provide an assessment of the impact of workload manipulations on various cardiac measurements. We further sought to determine the most effective measurement approaches of cognitive workload as well as quantify the conditions under which these measures are most effective for interpretation. Cognitive workload affects human performance, particularly when load is relatively high (overload) or low (underload). Despite ongoing interest in assessing cognitive workload through cardiac measures, it is currently unclear which cardiac-based assessments best indicate cognitive workload. Although several quantitative studies and qualitative reviews have sought to provide guidance, no meta-analytic integration of cardiac assessment(s) of cognitive workload exists to date. We used Morris and DeShon's meta-analytic procedures to quantify the changes in cardiac measures due to task load conditions. Sample-weighted Cohen's d values suggest that several metrics of cardiac activity demonstrate sensitivity in response to cognitive workload manipulations. Heart rate variability measures show sensitivity to task load, conditions of event rate, and task duration. Authors of future work should seek to quantify the utility of leveraging multiple metrics to understand workload. Results suggest that assessment of cognitive workload can be done using various cardiac activity indicators. Further, given the number of valid and reliable measures available, researchers and practitioners should base their selection of a psychophysiological measure on the experimental and practical concerns inherent to their task/protocol. Findings bear implications for future assessment of cognitive workload within basic and applied settings. Future research should seek to validate conditions under which measurements are best interpreted, including but not limited to individual differences.

In the field of neuroeconomics, the assessment of cognitive workload is a crucial issue with significant implications for real-world applications. Previous research has made progress in task-based germane cognitive load classification, but decentralized studies focusing on task-independent assessment have often produced less than optimal results. In this study, we present a stacked graph attention convolutional networks (SGATCNs) model to tackle the challenges related to task-independent cognitive workload assessment using EEG spatial information. The model employs the differential entropy (DE) and power spectral density (PSD) features of each EEG channel across four frequency bands (delta, theta, alpha, and beta) as node information. For the construction of the network structure, phase-locked values (PLVs), phase-lag indices (PLIs), Pearson correlation coefficients (PCCs), and mutual information (MI) are utilized and evaluated to generate a functional brain network. Specifically, the model aggregates spatial information on the dynamic map by stacking the graph attention layers and utilizes the convolution module to extract the frequency domain information from between the networks under each frequency band. We conducted a cognitive workload experiment with 15 subjects and selected three representative psychological experimental task paradigms (N-back, mental arithmetic, and Sternberg) to induce different levels of cognitive workload (low, medium, and high). Our framework achieved an average accuracy of 65.11% in recognizing the task-independent cognitive workload across the three scenarios.

High-fidelity clinical simulations can be used by clinicians to acquire technical (physical ability and knowledge) and non-technical (cognitive and social processes) skills. Excessive cognitive workload contributes to medical errors because of the impact on both technical and non-technical skills. Many studies measure cognitive workload with psychometric instruments that limit the assessment of cognitive workload to a single time period and may involve response bias. Using eye tracking to measure task-evoked pupillary responses allows the measurement of changes in pupil diameter related to the cognitive workload associated with a specific activity. Incorporating eye tracking with high-fidelity clinical simulations provides a reliable and continuous assessment of cognitive workload. The purpose of this literature review is to summarize the use of eye-tracking technology to measure cognitive workload of healthcare providers to generate evidence-based guidelines for measuring cognitive workload during high-fidelity clinical simulations. What this manuscript adds to the body of literature is a summary of best practices related to the different methods of measuring cognitive workload, benefits and limitations of using eye tracking, and high-fidelity clinical simulation design considerations for successful integration of eye tracking.

The topic of brain-computer interface is gaining increasing attention annually, yet there is limited research on confirming the reliability of Chinese indigenous chips in EEG signal acquisition. To demonstrate the progressiveness of Chinese indigenous chips, this article compares the performance of two ADC, ADS1299 and LHE7909, in EEG applications, with ADS1299 already proven reliable in multiple studies. Both chips demonstrate reliability in capturing high-quality EEG signals with the measured amplitude error below 2%. They show remarkably similar characteristics in the performance of SSVEP, oddball experiments and cognitive workload assessments on five participants from both genders and various age groups. LHE7909 even excels in capturing subtle EEG features such as the third harmonic components evoked in SSVEP in response to 13Hz stimulation, particularly in cognitive workload assessment, though tending to generate slightly more low-frequency noise. When it comes to P300 detection, LHE7909 shows a positive wave at approximately 340ms after the stimuli, in contrast to the ADS1299 which has a 360ms latency. These findings underscore the potential advantages of Chinese indigenous chips in EEG research.

The battlefield of the future will require the warfighter to multitask in numerous ways, seriously taxing the cognitive and perceptual capabilities of even the most advanced warrior. A principal concern in developing a better understanding of how current and proposed computational technologies can supplement and augment human performance in this and other environments is determining when such assistance is required. This challenge can be parsed into 2 components: determining what set of measurements accurately reflects cognitive state, and identifying techniques for synthesizing this set of measurements into a single collective cognitive state variable. The primary thesis of this proposal is that automatic human behavioral responses serve as inherent probes for cognitive state. Further, the human perception-action system is uniquely designed to capture, process, integrate, and act on an extraordinarily diverse range of information freely available in the natural environment. Together, this system and the surrounding environment which acts on it-and on which the system acts-form a dynamic coupling. Under normal conditions these couplings remain intact. When stressed, these couplings become degraded. Based on this understanding, the authors propose a unique suite of Cognitive Workload Assessment (CWA) tools, based on real-time measurements of postural control that can serve as both a stand-alone indicator of cognitive state as well as a cueing filter for engaging other CWA sensor suites that are currently under development.

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, β + 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.

Task difficulty and physiological measures of mental workload in air traffic control: a scoping review

Minimally invasive surgical techniques, including laparoscopic and robotic surgery, have profoundly impacted surgical practice by improving precision, reducing recovery times, and minimizing complications. However, these modalities differ in their cognitive demands and skill acquisition requirements, which can influence the learning curve and operative performance. To assess and evaluate this variability across these modalities, a functional near-infrared spectroscopy (fNIRS) system is used as an objective method for monitoring cognitive activity in surgical trainees. fNIRS can provide insights and further our understanding of the mental demands of different surgical techniques and their association with varying task complexity. This study seeks to assess the influence of surgical modality (laparoscopy vs. robotic surgery) and task complexity (pick and place (PP) vs. knot tying (KT)) on cognitive workload through fNIRS. We compare real-world and simulation-based training environments to determine changes in brain activation patterns and task performance. A total of twenty-six surgical trainees (general and gynecologic surgery residents and specialists) participated in this study. Participants completed standardized laparoscopic and robotic surgical tasks at varying levels of complexity while their cognitive workload was measured using fNIRS. This study included both simulation-based training and real-world surgical environments. Hemodynamic responses in the prefrontal cortex (PFC), task completion times, and performance metrics were analyzed. Laparoscopic surgery elicited higher activity changes in the prefrontal cortex, indicating increased cognitive demand compared with robotic surgery, particularly for complex tasks like knot tying. Task complexity significantly influenced mental load, with more intricate procedures eliciting greater neural activation. Real-world training resulted in higher cognitive engagement than simulation, emphasizing the gap between simulated and actual surgical performance. Cognitive workload was lower and significantly different during robotic surgery than during laparoscopy, potentially due to its ergonomic advantages and enhanced motor control. Simulation-based training effectively prepares surgeons, but the cognitive workload results indicate that it may not fully replicate real-world surgical environments. These findings reveal the importance of cognitive workload assessment in surgical education and suggest that incorporating neuroimaging techniques such as fNIRS into training programs could enhance skill acquisition and performance.

This text aims to present a study which deals with mental workload evaluation during task execution. It is focused on the Air Traffic Controllers working situation. In this document, we mainly introduce an experiment which has been conducted in a French En-Route air traffic center with the participation of Air Traffic Controllers. Four principal experiment characteristics are detailed: the experiment procedure, the working situation elaborated for our experimentation, the nature of the task achieved by participants, and the technique chosen to analyze mental workload felt by operators. We finally present the main results from our first data analysis which seem to confirm major observations known in the field of air traffic control, as well as, mental workload study field.

Multiple Remote Tower Operations (MRTOMRTO) change the way air traffic is managed. In this concept, air traffic control officers (ATCOs) operate several aerodromes simultaneously from a specially designed working position, also referred to as a multiple remote tower module (MRTMMRTM). This change in operations also introduces significant changes in the ATCOs’ workflow and cognitive demands. In theory MRTO can facilitate the ATCOs’ ability to balance their mental workloadWorkload through a flexible allocationallocation of aerodromesFlexible allocation of aerodromes to each MRTM, but new procedures need to be implemented to enable such flexible allocations: Appropriate handover proceduresHandover procedures are needed to transfer aerodromes between MRTMs and their operators. This paper investigated the feasibility of handover procedures during simulated air traffic control as a mitigation to counteract inappropriate mental workload. In a human-in-the-loop real-time simulation, six ATCOs completed traffic scenarios with or without handover via two MRTM, dealing with a total of three aerodromes. Descriptive data showed no adverse short-term effects caused by the handovers and indicated possible beneficial long-term effects on cognitive capacityCognitive capacity and safety. The handover procedures were overall feasible and accepted by the ATCOs, as a strategy to better balance mental workload in MRTO.

Mental workload during high-effort tasks is a crucial measure of effective task performance. Maintaining cognitive demands within an individual's capacity enables the effective performance of complex multi-tasking. Conversely, surpassing this margin may lead to unpredictable and suboptimal performance. This review explores human mental workload assessment across real-world tasks like aircraft piloting, vehicle driving under challenging conditions, and automated power plant monitoring. Both subjective and objective monitoring methods are examined: subjective methods include the widely used NASA-TLX, while objective methods cover EEG and eye-tracking measures during task execution. Findings from these methods are correlated with overall task performance outcomes. Real-time workload monitoring provides distinct advantages in critical settings; for instance, if an operator’s cognitive capacity is reached, alerts could be triggered, or task demands adjusted to alleviate overload. Finally, a multi-modal analysis of the literature is presented, comparing the effectiveness of various monitoring technologies across different task domains to guide future research directions.

With growing air traffic density, air-traffic controllers (ATCOs) are facing more challenges in interpreting and analyzing air traffic information. As one of the solutions to this problem, automation supports such as tactile human computer interface, interactive 3D radar displays, and conflict resolution aid (CRA) are proposed for the enhancement of the current air traffic control (ATC) systems. To evaluate the proposed ATC systems, questionnaires are commonly used to get the feedback from ATCOs. However, the questionnaires are usually administered upon completion of each ATC simulation task thus provide only overall ratings towards the new ATC systems. In this paper, we propose and implement a novel Electroencephalogram (EEG)-based neurocognitive tools for evaluation of ATC systems. The nature of EEG-based technique is that the brain states can be monitored in a high resolution time, fitting the nature of time-critical ATC tasks. Thus, such EEG-based human factors study allows for real-time monitoring of ATCOs' mental workload during the task performance in ATC systems. We designed and conducted an experiment to evaluate the costs and benefits of the CRA and tactile user interface in future ATC systems. Thirty six participants participated in the experiment and were assigned into three groups of different display modes: Non-Display, Display, and Trajectory Prediction. In each group, three CRA conditions were given: Manual, Reliable and Unreliable. The EEG data were recorded during the tasks, and the traditional workload evaluation method NASA Task Load Index (TLX) was administered at the end of each task. The result shows that the ratings obtained from NASA TLX and from the EEG labeling are highly correlated. Thus, the EEG-based system is reliable to recognize workload during the task performance. With the proposed EEG-based system, we found that 1) relatively high workload was observed at the beginning of the experiment in each group which could be due to participants' unfamiliarity with the interfaces; 2) participants in the trajectory prediction group had much higher workload as compared to vertical display and non-display groups, which could be attributed to its complexity. The changes of workload are due to interaction between the type of display modes and time (p<0.05); 3) workload varied slightly with different CRA setting. The results show that the proposed EEG-based system for human factors study can provide better understanding of real-time mental workload changes during the task performance in new ATC systems, therefore enables the evaluation of current and future ATC systems.

The interaction between the driver and the in-vehicle information system (IVIS) significantly impacts both the driver’s behavior and driving safety. This research aims to explore the influence of task complexity in touchscreen-based IVIS on driving behavior and mental workload. Based on the driving simulation, a test was designed for drivers to manually operate the IVIS under car-following conditions. Driving behavior and eye movement data were collected under different manual touchscreen task complexities. The effects of driver gender, driving experience, and the number of hands-on operations on driving behavior and mental workload were investigated by analysis of variance. A quantitative model of driver mental workload was constructed using exploratory factor analysis. The validity of this model was confirmed through the NASA-TLX scale. Additionally, a driver mental workload discrimination model was constructed using the Bayes discrimination method to achieve an accurate classification of mental workload. The results show that IVIS touchscreen operation tasks significantly affect driving behavior. The quantitative model, based on driving behavior and eye movement indicators, facilitates the evaluation of the driver’s mental workload under the influence of manual IVIS touchscreen operation tasks. Variables such as the driver’s gender, driving experience, and the number of hands-on operations significantly affect mental workload. The driver mental workload discrimination model correctly discriminated 87.0% of driver test samples. Cross-validation results showed an accuracy rate of 84.30%, indicating the model’s high precision in discriminating mental workload.

Purpose Despite the importance of having knowledge about a child’s cognitive functioning, less than one-third of children with cerebral palsy (CP) are formally assessed. Consequently, the cognitive strengths of many children with severe CP may be underestimated or go overlooked. This case study aimed to test accommodations to cognitive assessment administration procedures to enable switch access. Methods A 9-year-old boy with dyskinetic CP tested a suite of cognitive assessments with accommodations for single switch access and measures of user experience. The cognitive assessment included: receptive vocabulary; non-verbal reasoning; sustained attention; executive functions of problem solving and shifting; and visual perception skills. Results The participant’s ability to independently undertake assessment on the receptive vocabulary, non-verbal reasoning and the sustained attention measures indicates that accommodations made for single switch access were appropriate. Assessment took 1–2 h longer than expected for a typically developing child via standardised administration procedures, but was considerably faster than expected if undertaken via low-tech partner assisted scanning. Accessibility barriers continued to be present for the executive function and visual perception measures. Overall, the user experience was positive, both in regards to usability and cognitive load. Conclusions This case study provides emerging data for usability and accessibility of accommodations to a battery of cognitive assessment tasks. Further research is needed to devise appropriate accommodations for executive function and visual perception measures and to determine whether the accommodations are accessible more generally for children with motor and/or speech impairments. Implications for rehabilitation Accommodations can be successfully made to receptive vocabulary, non-verbal reasoning and sustained attention assessment administration procedures for switch technologies. Cognitive assessment with switch accommodations takes considerably longer to complete than standardised administration estimates for a typically developing child. Assessment may need to be scheduled over more than one session. User experience, including usability and cognitive load, of accommodations was positive.

With the increasing complexity of human-computer interaction systems, the cognitive load caused by interface information overload has become a key bottleneck affecting user experience and operational efficiency. Therefore, this paper proposes an interactive interface adaptive design model based on dynamic cognitive load evaluation and constructs a closed-loop optimization framework of “perception-evaluation-decision-execution”. Therefore, this paper proposes an interactive interface adaptive design model based on the dynamic evaluation of cognitive load, and constructs a closed-loop optimization framework of “perception evaluation decision execution”. First, a dynamic multimodal cognitive load assessment model is designed, which integrates behavioral, eye-movement, and physiological signals via a cross-modal attention mechanism, combined with time series modeling and uncertainty estimation, to achieve continuous and accurate perception of cognitive load. The root mean square error of prediction is 0.592. Second, a cognitive-driven interface adaptive decision-making framework is constructed, which uses the results of cognitive load assessment and task context as the basis for decision-making. On this basis, a cognitive-constrained reinforcement learning optimization algorithm is proposed. By introducing an upper-limit constraint on cognitive load and a strategy clipping mechanism, interaction efficiency is guaranteed and decision stability is improved. Experimental results show that the proposed method reduces task completion time by 22.7%, increases user satisfaction by 41.9%, decreases cognitive load by 25.0%, and keeps total response delay within 81 milliseconds. This study provides a systematic solution for the construction of intelligent adaptive interface with both evaluation accuracy and decision stability.