Cognitive Workload Assessment Research Articles

Using Eye Tracking for Measuring Cognitive Workload During Clinical Simulations: Literature Review and Synthesis.

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.

Multimodal Fusion for Objective Assessment of Cognitive Workload: A Review.

Considerable progress has been made in improving the estimation accuracy of cognitive workload using various sensor technologies. However, the overall performance of different algorithms and methods remain suboptimal in real-world applications. Some studies in the literature demonstrate that a single modality is sufficient to estimate cognitive workload. These studies are limited to controlled settings, a scenario that is significantly different from the real world where data gets corrupted, interrupted, and delayed. In such situations, the use of multiple modalities is needed. Multimodal fusion approaches have been successful in other domains, such as wireless-sensor networks, in addressing single-sensor weaknesses and improving information quality/accuracy. These approaches are inherently more reliable when a data source is lost. In the cognitive workload literature, sensors, such as electroencephalography (EEG), electrocardiography (ECG), and eye tracking, have shown success in estimating the aspects of cognitive workload. Multimodal approaches that combine data from several sensors together can be more robust for real-time measurement of cognitive workload. In this article, we review the published studies related to multimodal data fusion to estimate the cognitive workload and synthesize their main findings. We identify the opportunities for designing better multimodal fusion systems for cognitive workload modeling.

Caffeine Gum Does Not Improve Marksmanship, Bound Duration, Susceptibility to Enemy Fire, or Cognitive Performance During Tactical Combat Movement Simulation.

Military personnel supplement caffeine as a countermeasure during unavoidable sustained wakefulness. However, its utility in combat-relevant tasks is unknown. This study examined the effects of caffeinated gum on performance in a tactical combat movement simulation. Healthy men (n = 30) and women (n = 9) (age = 25.3 ± 6.8 years; mass 75.1 ± 13.1 kg) completed a marksmanship with a cognitive workload (CWL) assessment and a fire-andmove simulation (16 6-m bounds) in experimental conditions (placebo versus caffeinated gum, 4mg/kg). Susceptibility to enemy fire was modeled on bound duration during the fireand- move simulation. Across both conditions, bound duration and susceptibility to enemy fire increased by 9.3% and 7.8%, respectively (p = .001). Cognitive performance decreased after the fire-and-move simulation across both conditions (p < .05). However, bound duration, susceptibility to enemy fire, marksmanship, and cognitive performance did not differ between the caffeine and placebo conditions. These data do not support a benefit of using caffeinated gum to improve simulated tactical combat movements.

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

Cognitive workload assessment plays an important role to examine the mental status of brain for emergency situations faced by air traffic controllers, military personals and many more. It is also significant for brain disorder diagnosis and psychological health monitoring. Mental workload induced by various factors needs to be critically examined for better decisions. This paper presents an automated model for cognitive workload assessment to provide accurate categorization on intensity of workload induced by multitasking situations. Statistical wavelets corresponding to brain frequencies act as features for categorization task. The brain analysis needs technical expertise to identify the relevant information but, due to lack of sufficient number of experts, feature selection model helps to identify the relevant features responsible to categorize cognitive workload. Teaching Learning based Optimization (TLBO) method has been modified using ‘Logical Operators’ for binary feature selection problem in the current study and evaluated based on different parameters. Results on Logical TLBO reached upto 100% for binary and multiclass classification of cognitive workload. The results also show the proposed method is converging in minimum iteration for multiclass cognitive workload. PROMETHEE based multi-criteria decision-making approach has adopted for ranking of algorithm to select the best algorithm when multiple alternatives are available. Based on study of current literature, this approach appears to be first time implemented to provide ranking of optimization algorithms. The PROMETHEE method also suggests that the Logical TLBO is best among all the other compared approaches.

Heart rate variability based assessment of cognitive workload in smart operators

Heart rate variability based assessment of cognitive workload in smart operators

Sensor Location Optimization of Wireless Wearable fNIRS System for Cognitive Workload Monitoring Using a Data-Driven Approach for Improved Wearability.

Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive workload assessment receiving popularity due to its easier implementation, non-invasiveness, low cost and other benefits from the signal-processing point of view. Wearable wireless fNIRS systems used in research have promisingly shown that fNIRS could be used in cognitive workload assessment in out-of-the-lab scenarios, such as in operators’ cognitive workload monitoring. In such a scenario, the wearability of the system is a significant factor affecting user comfort. In this respect, the wearability of the system can be improved if it is possible to minimize an fNIRS system without much compromise of the cognitive workload detection accuracy. In this study, cognitive workload-related hemodynamic changes were acquired using an fNIRS system covering the whole forehead, which is the region of interest in most cognitive workload-monitoring studies. A machine learning approach was applied to explore how the mean accuracy of the cognitive workload classification accuracy varied across various sensing locations on the forehead such as the Left, Mid, Right, Left-Mid, Right-Mid and Whole forehead. The statistical significance analysis result showed that the Mid location could result in significant cognitive workload classification accuracy compared to Whole forehead sensing, with a statistically insignificant difference in the mean accuracy. Thus, the wearable fNIRS system can be improved in terms of wearability by optimizing the sensor location, considering the sensing of the Mid location on the forehead for cognitive workload monitoring.

LJaya optimisation‐based channel selection approach for performance improvement of cognitive workload assessment technique

In this Letter, the Logical Jaya optimisation is proposed as an extension of the Jaya optimisation algorithm to improve the cognitive workload (CW) assessment technique where channel selection for the EEG signal act as a binary optimisation problem. Channel selection is very crucial, time-consuming and requires expertise, specially when brain cognitive load is considered. The proposed approach is designed such that it not only improves the performance of the assessment model of CW but also reduces the computational cost. The approach also helps in the automation of brain analysis. The results obtained show that performance is improved by 22% than existing approaches to an average of >90% accuracy in different scenarios. The channels obtained using the approach also provided accurate active brain regions during CW analogous to previous studies.

Dynamic assessment of control room operator's cognitive workload using Electroencephalography (EEG)

In modern plants with high levels of automation, acquiring an adequate mental model of the process has become a challenge for operators. Studies indicate that sub-optimal decisions occur when there is a mismatch between the demands of the process and the human's capability. This mismatch leads to high cognitive workload in human operators, often a precursor for poor performance. Recently, researchers in various safety critical domains (aviation, driving, marine, NPP, etc.) have started to explore the use of physiological measurements from humans to understand their cognitive workload and its effect. In this work, we evaluate the potential of EEG to measure cognitive workload of human operators in chemical process control room. We propose a single dry electrode EEG based methodology for identifying the similarities and mismatch between the operators’ mental model of the process and the actual process behaviour during abnormal situations. Our results reveal that SƟ(ω), the power spectral density of theta (ɵ) waves (frequency range 4–7 Hz) in the EEG signal has the potential to identify such mismatches. Results indicate that SƟ(ω) is positively correlated with workload and hence can be used for assessing the cognitive workload of operators in process industries.

Dynamic cognitive workload assessment for fighter pilots in simulated fighter aircraft environment using EEG

The trend in aviation automation demands more mental workload of pilots, in addition to their routine manoeuvring task. For fighter aircraft pilots, the mental workload multifold due to read-back or hear-back with precise weapon handling in combat scenarios. Assessing the pilot cognitive workload is an important aspect, as it influences the pilot performance, and this is an error intolerance environment. This study aims to assess fighter aircraft Pilot’s Cognitive WorkLoad (PCWL) and attention when they exposed to a dynamic workload environment; by monitoring the neuronal activities. Here the spectral Electroencephalographic (EEG) features were extracted to assess the dynamic workload (normal, moderate, high, and very high workload), and attention monitored by National Aeronautics and Space Administration-Task Load Index (NASA-TLX) and it serves as a validation for cognitive findings. Also, brain source localization obtained from EEG inverse calculation technique, Standardized Low-Resolution brain Electromagnetic Tomography (sLORETA) performed to understand the coherence of workload and neuronal activity engagement concerning brain lobes. Statistical significance (p < 0.05) of EEG feature sets indicate that the approach and withdrawal behaviour of pilots are getting modulated during the different workload of the flying conditions. Further, NASA-TLX findings are well correlated and indicate a cognitive demand during high and very high workload. Furthermore, EEG findings correlated with the sLORETA study, which shows the engagement of prefrontal sensorimotor, fronto-temporal, and parietal regions during various flying conditions, under different cognitive loading. In the future, these findings could be adopted for pilot assessment and as a validation tool of the pilot's training program.

Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface.

Cognitive workload is one of the widely invoked human factors in the areas of human–machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.31% average accuracy for brain–computer interface (BCI). The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain. We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant. Real-time four-level MWL states are assessed using fNIRS system, and initial classification is performed using three strong machine learning (ML) techniques, support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural network (ANN) with obtained average accuracies of 54.33, 54.31, and 69.36%, respectively. In this study, novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 87.45 and 89.31% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem. Statistical analysis, t-test, and one-way F-test (ANOVA) are also performed on accuracies obtained through ML and DL algorithms. Results show that the proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared with ML (SVM, ANN, and k-NN) algorithms.

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments.

Background: Although many electroencephalographic (EEG) indicators have been proposed in the literature, it is unclear which of the power bands and various indices are best as indicators of mental workload. Spectral powers (Theta, Alpha, and Beta) and ratios (Beta/(Alpha + Theta), Theta/Alpha, Theta/Beta) were identified in the literature as prominent indicators of cognitive workload.Objective: The aim of the present study is to identify a set of EEG indicators that can be used for the objective assessment of cognitive workload in a multitasking setting and as a foundational step toward a human-autonomy augmented cognition system.Methods: The participants' perceived workload was modulated during a teleoperation task involving an unmanned aerial vehicle (UAV) shepherding a swarm of unmanned ground vehicles (UGVs). Three sources of data were recorded from sixteen participants (n = 16): heart rate (HR), EEG, and subjective indicators of the perceived workload using the Air Traffic Workload Input Technique (ATWIT).Results: The HR data predicted the scores from ATWIT. Nineteen common EEG features offered a discriminatory power of the four workload setups with high classification accuracy (82.23%), exhibiting a higher sensitivity than ATWIT and HR.Conclusion: The identified set of features represents EEG indicators for the objective assessment of cognitive workload across subjects. These common indicators could be used for augmented intelligence in human-autonomy teaming scenarios, and form the basis for our work on designing a closed-loop augmented cognition system for human-swarm teaming.

Ternary-task convolutional bidirectional neural turing machine for assessment of EEG-based cognitive workload

Cognitive workload plays a crucial role in the observation of mental activity which has great significance in brain-computer interfaces (BCI), cognitive neuroscience and biomedical fields. The estimation of cognitive state based on the classification of the electroencephalograph (EEG) is a hot issue received more and more attention. So far, a variety of Deep Learning models have been raised, which has yielded improvements in feature extraction and classification. However, the existing models reveal shortcomings in processing spatial, spectral, and temporal features of the EEG. In this paper, we propose a deep hybrid Network called Ternary-task Convolutional Bidirectional Neural Turing Machine (TT-CBNTM) to perform cognitive state assessment. First, TT-CBNTM consists of Convolutional Neural Network (CNN) and Bidirectional Neural Turing Machine (BNTM), of which CNN is applied to preserve the spatial and spectral representations of EEG, while the BNTM is applied to learn temporal representations from features which are extracted from the CNN. Second, we propose a new strategy called ternary-task regularization framework to induce the overfitting on the EEG database. The main task is to assess EEG-based cognitive workload through classification of EEG signals. The auxiliary tasks is identification and verification, through which we can increase the inter-class variations and reduce the intra-class variations, further lead to a better result of the EEG-based cognitive workload classification. The classication accuracy of our TT-CBNTM is 96.3 %, yielding 5 % improvement over the state-of-the-art models. This demonstrates the significant effectiveness of our approach which can be applied successfully to cognitive monitoring systems.

Cardiac Measures of Cognitive Workload: A Meta-Analysis.

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.

An Agile Approach to Improve the Usability of a Physical Telerehabilitation Platform

The goal of a telerehabilitation platform is to safely and securely facilitate the rehabilitation of patients through the use of telecommunication technologies complemented with the use of biomedical smart sensors. The purpose of this study was to perform a usability evaluation of a telerehabilitation platform. To improve the level of usability, the researchers developed and proposed an iterative process. The platform uses a digital representation of the patient which duplicates the therapeutic exercise being executed by the patient; this is detected by a Kinect camera and sensors in real time. This study used inspection methods to perform a usability evaluation of an exploratory prototype of a telerehabilitation platform. In addition, a cognitive workload assessment was performed to complement the usability evaluation. Users were involved through all the stages of the iterative refinement process. Usability issues were progressively reduced from the first iteration to the fourth iteration according to improvements which were developed and applied by the experts. Usability issues originally cataloged as catastrophic were reduced to zero, major usability problems were reduced to ten (2.75%) and minor usability problems were decreased to 141 (38.74%). This study also intends to serve as a guide to improve the usability of e-Health systems in alignment with the software development cycle.

Cusp catastrophe models for cognitive workload and fatigue in teams

The use of two cusp catastrophe models has been effective for untangling the effects of cognitive workload, fatigue, and other complications on the performance of individuals. This study is the first to use the two models to separate workload and fatigue effects on team performance. In an experiment involving an emergency response simulation, 360 undergraduates were organized into 44 teams. Workload was varied by team size, number of opponents, and time pressure. The cusp models for workload and fatigue were more accurate for describing trends in team performance criteria compared to linear alternatives. Individual differences in elasticity-rigidity were less important than subjective workload and experimental conditions as control variables. Fluid intelligence within the team was an important compensatory ability in the fatigue model. Results further supported the nonlinear paradigm for the assessment of cognitive workload and fatigue and demonstrated its effectiveness for understanding team phenomena.

Virtual simulation and learning new skills in video-assisted thoracic surgery

Background: The effectiveness of training video-assisted thoracic surgery (VATS) resident surgeons using virtual reality (VR) simulation is stated in many studies, however its use is still not established in the normal practice. The purpose of this study is to create a VR curriculum to offer an evidence-based approach for VATS training programs. Methods: Skills were evaluated with two tests: Objective Structured Assessment of Technical Skill (OSATS) and Global Operative Assessment of Thoracoscopic Skills (GOATS). Surgeons were evaluated for cognitive workload according to National Aeronautics Space Administration-Task Load Index (NASA-TLX). Subjects were stratified into two groups: trainees and consultants. Differences in performance between groups were analyzed using the Kruskal-Wallis test for nonparametric data. Results: In total 20 voluntaries completed all tasks (trainees =12, consultant =8). Comparisons between trainee and consultant groups showed similar results in all tests on P values. OSATS and GOATS performance of both groups were similar without skills differences regarding experience. Median scores of experienced surgeons were taken as benchmark levels. Comparison of the novices’ scores with benchmark levels showed that all were able to achieve the set criteria. The Kiviat diagram of the NASA-TLX cognitive workload assessment proved a greater mental and physical demand in the trainees. Nevertheless, these variations between groups were not significantly different. Conclusions: VR training can shorten the learning curve, even if is not designed to replace the experience gained in the operating theatre. A VATS training curriculum with VR assessment allows trainees to get acquainted, train and learn the VATS lobectomy technique. This study supports clearly the inclusion of VR simulation into surgical training programs.

Measurement of attentional reserve and mental effort for cognitive workload assessment under various task demands during dual-task walking

Previous work focused on cognitive workload assessment suggests EEG spectral content and component amplitudes of the event-related potential (ERP) waveform may index mental effort and attentional reserve, respectively. Although few studies have assessed attentional reserve and mental effort during upper-extremity performance, none have employed a combined approach to measure cognitive workload during locomotion. Therefore, by systematically considering ERPs, spectral content and importantly their combination, this study aimed to examine whether concurrent changes in spectral content and ERPs could collectively serve as an index of cognitive workload during locomotion. Specifically, ERP and EEG biomarkers were assessed as participants performed a cognitive task under two levels of difficulty (easy or hard) and two conditions (seated or walking). Changes in attentional reserve and mental effort appeared to collectively index cognitive workload under varying demands due to changes in task difficulty or performance conditions. This work can inform cognitive workload assessment in patient populations with gait deficiencies for future applications.

FNIRS differentiates cognitive workload between concussed adolescents and healthy controls

fNIRS differentiates cognitive workload between concussed adolescents and healthy controls

Human Neuro-Activity for Securing Body Area Networks: Application of Brain-Computer Interfaces to People-Centric Internet of Things

A former definition states that a brain-computer Interface provides a direct communication channel to the brain without the need for muscles and nerves. With the emergence of wearable and wireless brain-computer interfaces, these systems have evolved to become part of wireless body area networks, offering people-centric applications such as cognitive workload assessment and detection of selective attention. Currently, wireless body area networks are mostly integrated by low-cost devices that, because of their limited hardware resources, cannot generate secure random numbers for encryption. This is a critical issue in the context of new Internet of Things device communication and its security. Such devices require securing their communication, mostly by means of the automatic renewal of the cryptographic keys. In the domain of the people-centric Internet of Things, we propose to use wireless brain-computer interfaces as a secure source of entropy, based on neuro- activity, capable to generate secure keys that outperforms other generation methods. In our approach, current wireless brain-computer interface technology is an attractive option to offer novel services emerged from novel necessities in the context of the people-centric Internet of Things. Our proposal is an implementation of the human-in-the-loop paradigm, in which devices and humans indistinctly request and offer services to each other for mutual benefit.

MP11-05 IS SEEING BELIEVING? TECHNICAL MENTORSHIP DURING ROBOT-ASSISTED SURGERY

You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment I1 Apr 2016MP11-05 IS SEEING BELIEVING? TECHNICAL MENTORSHIP DURING ROBOT-ASSISTED SURGERY Ahmed Hussein, Somayeh Shafiei, Mohamed Sharif, Basel Ahmad, Ehsan Esfahani, and Khurshid Guru Ahmed HusseinAhmed Hussein More articles by this author , Somayeh ShafieiSomayeh Shafiei More articles by this author , Mohamed SharifMohamed Sharif More articles by this author , Basel AhmadBasel Ahmad More articles by this author , Ehsan EsfahaniEhsan Esfahani More articles by this author , and Khurshid GuruKhurshid Guru More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.2376AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Cognitive and mental workload assessment may play a critical role in defining successful mentorship. We sought to elucidate the cognitive performance metrics of surgical mentor during robot-assisted surgery, and to identify if viewing operative performance of surgical trainees is similar to performing robot-assisted surgery for a surgical mentor METHODS An IRB approved study enrolled 51 robot-assisted surgical procedures performed by single surgeon between 2013 and 2014. A 20-channel wireless electroencephalography (EEG) recording device was used to monitor brain activity using neuro-headset during all surgical procedures. During each task, participant’s cognitive engagement, mental workload and mental state were evaluated via wireless EEG recordings. Two key portions of interaction between trainee and robotic surgeon mentor while performing prostatectomy & cystectomy were included in the analysis: extended lymph node dissection (eLND) (n=21) and urethro-vesical anastomosis (UVA) (n=19). Live, intraoperative exchange between robotic surgeon mentor and trainee in field notes, NASA -TLX based subjective evaluations were also assessed. RESULTS Lymph Node Dissection: As the trainee surgeon felt that the procedure was more challenging (high mental and/or physical demands), he was more frustrated and had to put more effort and his perception of his performance was worse. Meanwhile the mentor surgeon’s workload was increased (continuous concern and more attention) towards the trainee’s performance. Urethro-vesical Anastomosis: Whenever the trainee felt the procedure was challenging (high mental, physical or temporal demands), the expert was paying more attention (higher mental state and lower distraction). As the trainee felt more operative challenges, the expert also felt it was both mentally and physically demanding. However, it did not affect the perception of expert on trainee frustration, effort and performance level. There was no significant difference between cognitive metrics of the surgeon while watching the trainee and while actually performing the same surgical steps. CONCLUSIONS Utilization of cognitive performance metrics during live intra-operative mentorship can provide insight into studying team behavior and interaction during complex demanding surgical performance. Further research is required into use of cognitive performance metrics to assess the relationship between performance, mentorship, and its educational impact. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e112-e113 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Ahmed Hussein More articles by this author Somayeh Shafiei More articles by this author Mohamed Sharif More articles by this author Basel Ahmad More articles by this author Ehsan Esfahani More articles by this author Khurshid Guru More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...