Temporal dynamics of autonomic nervous system responses under cognitive-emotional workload in obsessive-compulsive disorder.

Dysregulation of the autonomic nervous system (ANS) is commonly observed in various mental disorders, particularly when individuals engage in prolonged cognitive-emotional tasks that require ANS adjustment to workload. Although the understanding of the temporal dynamics of sympathetic and parasympathetic tones in obsessive-compulsive disorder (OCD) is limited, analyzing ANS reactions to cognitive-emotional workload could provide valuable insights into one of the underlying causes of OCD. This study investigated the temporal dynamics of heart rate (HR) and pupil area (PA) while participants with OCD and healthy volunteers solved antisaccade tasks, with affective pictures serving as central fixation stimuli. The data of 31 individuals with OCD and 30 healthy volunteers were included in the study, comprising three separate blocks, each lasting approximately 8 min. The results revealed an increase in sympathetic tone in the OCD group, with the most noticeable rise occurring during the middle part of each block, particularly during the presentation of negative stimuli. Healthy volunteers demonstrated adaptive temporal dynamics of HR and PA from the first block to the last block of tasks, whereas individuals with OCD exhibited fewer changes over time, suggesting a reduced adaptation of the ANS sympathetic tone to cognitive-emotional workload in OCD.

Comparison of cognitive workload and surgical outcomes between a three-dimensional and conventional microscope macular hole surgery.

BACKGROUND: Performing a surgical task subjects the surgeon to multitudinal stressors, especially with the newer 3D technology. The quantum of cognitive workload using this modern surgical system in comparison to the Conventional microscope system remains unexplored. We evaluate the surgeon's cognitive workload and the surgical outcomes of macular hole(MH) surgery performed on a 3D versus a Conventional microscope operating system. METHODS: 50 eyes of 50 patients with MH undergoing surgery using the 3D or Conventional microscope visualization system. Cognitive workload assessment was done by real-time tools(Surgeons' heart rate [HR] and oxygen saturation[SPO2]) and self-report tool(Surgery Task Load Index[SURG-TLX] questionnaire) of three Vitreoretinal surgeons. Based on the SURG-TLX questionnaire, an assessment of the workload was performed. RESULTS: Of the 50 eyes, 30 eyes and 20 eyes underwent surgery with the Conventional microscope and the 3D system, respectively. No difference was noted in the MH basal-diameter(p = 0.128), total surgical-duration(p = 0.299), internal-limiting membrane(ILM) peel time(p = 0.682), and the final visual acuity (VA; p = 0.515) between the two groups. Both groups showed significant improvement in VA(p < 0.001) with a 90% closure rate at one-month post-surgery. Cognitive workload comparison, the intraoperative HR(p = 0.024), total workload score(P = 0.005), and temporal-demand dimension(p = 0.004) were significantly more in Conventional microscope group as compared to 3D group. In both the groups, the HR increased significantly from the baseline while performing ILM peeling and at the end. CONCLUSION: The surgeon's cognitive workload is markedly reduced while performing macular hole surgery with a 3D viewing system. Moreover, duration of surgery including ILM peel time, MH closure rates, and visual outcomes remains unaffected irrespective of the operating microscope system.

A Review of the Use of Gaze and Pupil Metrics to Assess Mental Workload in Gamified and Simulated Sensorimotor Tasks.

Gaze and pupil metrics are used to represent higher cognitive processes in a variety of contexts. One growing area of research is the real-time assessment of workload and corresponding effort in gamified or simulated cognitive and motor tasks, which will be reviewed in this paper. While some measurements are consistent across studies, others vary and are likely dependent on the nature of the effort required by the task and the resulting changes in arousal. Pupil diameter is shown to consistently increase with task effort and arousal; however, the valence of arousal must be considered. In many cases, measures of pupil diameter were sensitive to both excessive and insufficient challenge. Overall, it is evident that gaze and pupil metrics are valuable to assess the cognitive state during gamified and simulated tasks, and further research is indicated regarding their use in clinical populations in rehabilitation to inform optimally engaging interventions.

Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability.

The adoption of Industry 4.0 technologies in manufacturing systems has accelerated in recent years, with a shift towards understanding operators' well-being and resilience within the context of creating a human-centric manufacturing environment. In addition to measuring physical workload, monitoring operators' cognitive workload is becoming a key element in maintaining a healthy and high-performing working environment in future digitalized manufacturing systems. The current approaches to the measurement of cognitive workload may be inadequate when human operators are faced with a series of new digitalized technologies, where their impact on operators' mental workload and performance needs to be better understood. Therefore, a new method for measuring and determining the cognitive workload is required. Here, we propose a new method for determining cognitive-workload indices in a human-centric environment. The approach provides a method to define and verify the relationships between the factors of task complexity, cognitive workload, operators' level of expertise, and indirectly, the operator performance level in a highly digitalized manufacturing environment. Our strategy is tested in a series of experiments where operators perform assembly tasks on a Wankel Engine block. The physiological signals from heart-rate variability and pupillometry bio-markers of 17 operators were captured and analysed using eye-tracking and electrocardiogram sensors. The experimental results demonstrate statistically significant differences in both cardiac and pupillometry-based cognitive load indices across the four task complexity levels (rest, low, medium, and high). Notably, these developed indices also provide better indications of cognitive load responding to changes in complexity compared to other measures. Additionally, while experts appear to exhibit lower cognitive loads across all complexity levels, further analysis is required to confirm statistically significant differences. In conclusion, the results from both measurement sensors are found to be compatible and in support of the proposed new approach. Our strategy should be useful for designing and optimizing workplace environments based on the cognitive load experienced by operators.

Cognitive Effort during Visuospatial Problem Solving in Physical Real World, on Computer Screen, and in Virtual Reality.

Spatial cognition plays a crucial role in academic achievement, particularly in science, technology, engineering, and mathematics (STEM) domains. Immersive virtual environments (VRs) have the growing potential to reduce cognitive load and improve spatial reasoning. However, traditional methods struggle to assess the mental effort required for visuospatial processes due to the difficulty in verbalizing actions and other limitations in self-reported evaluations. In this neuroergonomics study, we aimed to capture the neural activity associated with cognitive workload during visuospatial tasks and evaluate the impact of the visualization medium on visuospatial task performance. We utilized functional near-infrared spectroscopy (fNIRS) wearable neuroimaging to assess cognitive effort during spatial-reasoning-based problem-solving and compared a VR, a computer screen, and a physical real-world task presentation. Our results reveal a higher neural efficiency in the prefrontal cortex (PFC) during 3D geometry puzzles in VR settings compared to the settings in the physical world and on the computer screen. VR appears to reduce the visuospatial task load by facilitating spatial visualization and providing visual cues. This makes it a valuable tool for spatial cognition training, especially for beginners. Additionally, our multimodal approach allows for progressively increasing task complexity, maintaining a challenge throughout training. This study underscores the potential of VR in developing spatial skills and highlights the value of comparing brain data and human interaction across different training settings.

Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases.

Neurodegenerative diseases (NDs), such as Alzheimer's, Parkinson's, amyotrophic lateral sclerosis, and frontotemporal dementia, among others, are increasingly prevalent in the global population. The clinical diagnosis of these NDs is based on the detection and characterization of motor and non-motor symptoms. However, when these diagnoses are made, the subjects are often in advanced stages where neuromuscular alterations are frequently irreversible. In this context, we propose a methodology to evaluate the cognitive workload (CWL) of motor tasks involving decision-making processes. CWL is a concept widely used to address the balance between task demand and the subject's available resources to complete that task. In this study, multiple models for motor planning during a motor decision-making task were developed by recording EEG and EMG signals in n=17 healthy volunteers (9 males, 8 females, age 28.66±8.8 years). In the proposed test, volunteers have to make decisions about which hand should be moved based on the onset of a visual stimulus. We computed functional connectivity between the cortex and muscles, as well as among muscles using both corticomuscular and intermuscular coherence. Despite three models being generated, just one of them had strong performance. The results showed two types of motor decision-making processes depending on the hand to move. Moreover, the central processing of decision-making for the left hand movement can be accurately estimated using behavioral measures such as planning time combined with peripheral recordings like EMG signals. The models provided in this study could be considered as a methodological foundation to detect neuromuscular alterations in asymptomatic patients, as well as to monitor the process of a degenerative disease.

Measuring cognitive workload in the nuclear control room: a review.

Despite the substantial literature and human factors guidance, evaluators report challenges in selecting cognitive workload measures for the evaluation of complex human-technology systems. A review of 32 articles found that self-report measures and secondary tasks were systematically sensitive to human-system interface conditions and correlated with physiological measures. Therefore, including a self-report measure of cognitive workload is recommended when evaluating human-system interfaces. Physiological measures were mainly used in method studies, and future research must demonstrate the utility of these measures for human-system evaluation in complex work settings. However, indexes of physiological measures showed promise for cognitive workload assessment. The review revealed a limited focus on the measurement of excessive cognitive workload, although this is a key topic in nuclear process control. To support human-system evaluation of adequate cognitive workload, future research on behavioural measures may be useful in the identification and analysis of underload and overload.

This review provides background for the selection of cognitive workload measures for the evaluation of complex human­technology systems and identifies future research needs for applied cognitive workload assessment.

Dose-response of virtual reality training of paediatric emergencies in a randomised simulation-based setting.

AIM: To determine the effect of different virtual reality training intervals on individual performance to facilitate the optimal implementation of medical virtual reality training. METHODS: Emergency scenarios in virtual reality were performed by 36 medical students from the Medical University of Vienna. After baseline training, the participants were randomised into three groups of equal size and underwent virtual reality training at different time intervals (monthly, one training after 3 months, and no further training) before undergoing final assessment training after 6 months. RESULTS: Group A, with monthly training exercises, improved their performance score significantly by 1.75 mean score points compared with Group B, who repeated baseline training after 3 months. Statistically significant difference was indicated when comparing Group A with Group C, which was not further trained and served as the control group. CONCLUSION: One-month intervals are associated with statistically significant performance improvements compared with additional training after 3 months and to a control group without regular training. The results show that training intervals of 3 months or longer are insufficient to achieve high performance scores. Virtual reality training is a cost-effective alternative to conventional simulation-based training for regular practice.

Examining Care Planning Efficiency and Clinical Decision Support Adoption in a System Tailoring to Nurses' Graph Literacy: National, Web-Based Randomized Controlled Trial.

BACKGROUND: The proliferation of health care data in electronic health records (EHRs) is fueling the need for clinical decision support (CDS) that ensures accuracy and reduces cognitive processing and documentation burden. The CDS format can play a key role in achieving the desired outcomes. Building on our laboratory-based pilot study with 60 registered nurses (RNs) from 1 Midwest US metropolitan area indicating the importance of graph literacy (GL), we conducted a fully powered, innovative, national, and web-based randomized controlled trial with 203 RNs. OBJECTIVE: This study aimed to compare care planning time (CPT) and the adoption of evidence-based CDS recommendations by RNs randomly assigned to 1 of 4 CDS format groups: text only (TO), text+table (TT), text+graph (TG), and tailored (based on the RN's GL score). We hypothesized that the tailored CDS group will have faster CPT (primary) and higher adoption rates (secondary) than the 3 nontailored CDS groups. METHODS: Eligible RNs employed in an adult hospital unit within the past 2 years were recruited randomly from 10 State Board of Nursing lists representing the 5 regions of the United States (Northeast, Southeast, Midwest, Southwest, and West) to participate in a randomized controlled trial. RNs were randomly assigned to 1 of 4 CDS format groups-TO, TT, TG, and tailored (based on the RN's GL score)-and interacted with the intervention on their PCs. Regression analysis was performed to estimate the effect of tailoring and the association between CPT and RN characteristics. RESULTS: The differences between the tailored (n=46) and nontailored (TO, n=55; TT, n=54; and TG, n=48) CDS groups were not significant for either the CPT or the CDS adoption rate. RNs with low GL had longer CPT interacting with the TG CDS format than the TO CDS format (P=.01). The CPT in the TG CDS format was associated with age (P=.02), GL (P=.02), and comfort with EHRs (P=.047). Comfort with EHRs was also associated with CPT in the TT CDS format (P<.001). CONCLUSIONS: Although tailoring based on GL did not improve CPT or adoption, the study reinforced previous pilot findings that low GL is associated with longer CPT when graphs were included in care planning CDS. Higher GL, younger age, and comfort with EHRs were associated with shorter CPT. These findings are robust based on our new innovative testing strategy in which a diverse national sample of RN participants (randomly derived from 10 State Board of Nursing lists) interacted on the web with the intervention on their PCs. Future studies applying our innovative methodology are recommended to cost-effectively enhance the understanding of how the RN's GL, combined with additional factors, can inform the development of efficient CDS for care planning and other EHR components before use in practice.

Identifying the response process validity of clinical vignette-type multiple choice questions: An eye-tracking study.

INTRODUCTION: Clinical vignette-type multiple choice questions (CV-MCQs) are widely used in assessment and identifying the response process validity (RPV) of questions with low and high integration of knowledge is essential. Answering CV-MCQs of different levels of knowledge application and integration can be understood from a cognitive workload perspective and this can be identified by using eye-tracking. The aim of the pilot study was to identify the cognitive workload and RPV of CV-MCQs of different levels of knowledge application and integration by the use eye-tracking. METHODS: Fourteen fourth-year medical students answered a test with 40 CV-MCQs, which were equally divided into low-level and high-level complexity (knowledge application and integration). Cognitive workload was measured using screen-based eye tracking, with the number of fixations and revisitations for each area of interest. RESULTS: We found a higher cognitive workload for high-level complexity (M = 121.74) compared with lower-level complexity questions (M = 51.94) and also for participants who answered questions incorrectly (M = 94.31) compared with correctly (M = 79.36). CONCLUSION: Eye-tracking has the potential to become a useful and practical approach for helping to identify the RPV of CV-MCQs. This approach can be used for improving the design and development of CV-MCQs, and to provide feedback to inform teaching and learning.[Box: see text].

Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills.

Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.

Analysis of Relation between Brainwave Activity and Reaction Time of Short-Haul Pilots Based on EEG Data.

The purpose of this research is to examine and assess the relation between a pilot's concentration and reaction time with specific brain activity during short-haul flights. Participants took part in one-hour long flight sessions performed on the FNPT II class flight simulator. Subjects were instructed to respond to unexpected events that occurred during the flight. The brainwaves of each participant were recorded with the Emotiv EPOC+ Scientific Contextual EEG device. The majority of participants showed a statistically significant, positive correlation between Theta Power in the frontal lobe and response time. Additionally, most subjects exhibited statistically significant, positive correlations between band-power and reaction times in the Theta range for the temporal and parietal lobes. Statistically significant event-related changes (ERC) were observed for the majority of subjects in the frontal lobe for Theta frequencies, Beta waves in the frontal lobe and in all lobes for the Gamma band. Notably, significant ERC was also observed for Theta and Beta frequencies in the temporal and occipital Lobes, Alpha waves in the frontal, parietal and occipital lobes for most participants. A difference in brain activity patterns was observed, depending on the performance in time-restricted tasks.

A Neurophysiological Evaluation of Cognitive Load during Augmented Reality Interactions in Various Industrial Maintenance and Assembly Tasks.

Augmented reality (AR) has been shown to improve productivity in industry, but its adverse effects (e.g., headaches, eye strain, nausea, and mental workload) on users warrant further investigation. The objective of this study is to investigate the effects of different instruction methods (i.e., HoloLens AR-based and paper-based instructions) and task complexity (low and high-demanding tasks) on cognitive workloads and performance. Twenty-eight healthy males with a mean age of 32.12 (SD 2.45) years were recruited in this study and were randomly divided into two groups. The first group performed the experiment using AR-based instruction, and the second group used paper-based instruction. Performance was measured using total task time (TTT). The cognitive workload was measured using the power of electroencephalograph (EEG) features and the NASA task load index (NASA TLX). The results showed that using AR instructions resulted in a reduction in maintenance times and an increase in mental workload compared to paper instructions, particularly for the more demanding tasks. With AR instruction, 0.45% and 14.94% less time was spent on low- and high-demand tasks, respectively, as compared to paper instructions. According to the EEG features, employing AR to guide employees during highly demanding maintenance tasks increased information processing, which could be linked with an increased germane cognitive load. Increased germane cognitive load means participants can better facilitate long-term knowledge and skill acquisition. These results suggested that AR is superior and recommended for highly demanding maintenance tasks since it speeds up maintenance times and increases the possibility that information is stored in long-term memory and encrypted for recalls.

Facilitating the Work of Unmanned Aerial Vehicle Operators Using Artificial Intelligence: An Intelligent Filter for Command-and-Control Maps to Reduce Cognitive Workload.

OBJECTIVE: Evaluating the ability of a Gibsonian-inspired artificial intelligence (AI) algorithm to reduce the cognitive workloads of military Unmanned Aerial Vehicle (UAV) operators. BACKGROUND: Military UAV operators use the command-and-control (C2) map for developing mission-relevant situation awareness (SA). Yet C2 maps are overloaded with information, mostly irrelevant to the mission, causing operators to neglect the map altogether. To reduce irrelevant information, an intelligent filtering algorithm was developed. Here we evaluate its effectiveness in reducing operators' cognitive workloads. METHOD: Two-stage operational scenarios were conducted with professional ex-military UAV operators, using two filter protocols and a no-filter control. High-end real-time techniques were used to continuously assess workload from muscle behavior and machine learning models. RESULTS: Lower cognitive workload was found when applying the algorithm's protocols, especially when fatigue started to accumulate (Stage II). However, concerns about the quality of SA arose. CONCLUSION: The algorithm was positively evaluated for its ability to reduce operators' cognitive workloads. More evaluations of operators' SA are required. APPLICATION: The algorithm demonstrates the possibility of integrating AI to improve human performance in complex systems, and can be applied to other domains where spatial-temporal information needs to be contextually filtered in real time.

Effects of unreliable automation, non-driving related task, and takeover time budget on drivers' takeover performance and workload.

The objective of this study was to assess the effects of unreliable automation, non-driving related tasks (NDRTs), and takeover time budget (TOTB) on drivers' takeover performance and cognitive workload when faced with critical incidents. Automated vehicles are expected to improve traffic safety. However, there are still some concerns about the effects of automation failures on driver performance and workload. Twenty-eight drivers participated in a driving simulation study. The findings suggested that drivers require at least 8 s of TOTB to safely take over the control of the vehicle. In addition, drivers exhibited safer takeover performance under the conditionally automated driving situation than negotiating the critical incident in the manual driving condition. The results of drivers' cognitive workload were inconclusive, which might be due to the individual and recall biases in subjective measures that could not capture subtle differences in workload during takeover requests.Practitioner Summary: A driving simulation study was conducted to assess the effect of unreliable automation, non-driving related tasks, and different takeover time budgets on drivers' performance and workload. The results can provide guidelines for vehicle manufacturers to improve the design of automated vehicles.

Development and validation of a task load index for process control room operators (PCRO-TLX).

Process control room operators (PCRO) perform a range of complex cognitive safety-critical tasks. The aim of this exploratory sequential mixed methods study was to develop an occupation specific tool to measure the task load of PCRO using NASA Task Load Index (TLX) methodology. Participants were 30 human factors experts and 146 PCRO at two refinery complexes in Iran. Dimensions were developed via a cognitive task analysis, a research review, and three expert panels. Six dimensions were identified: perceptual demand, performance, mental demand, time pressure, effort, and stress. Data from 120 PCRO confirmed that the developed PCRO-TLX has acceptable psychometric properties, and a comparison with the NASA-TLX confirmed that perceptual, not physical, demand was relevant for measuring workload in PCRO. There was a positive convergence of scores of the Subjective Workload Assessment Technique and the PCRO-TLX. This reliable tool (α = 0.83) is recommended for risk assessing the task load of PCRO.Practitioner summary: There are benefits of having a specific tool to measure task load in safety critical roles. Thus, we developed and validated an easy-to-use targeted tool, the PCRO-TLX, for process control room operatives. Timely use and response will assure optimal production alongside health and safety in an organisation.Abbreviations: PCRO: process control room operator; TLX: task load index; PCRO-TLX: process control room operator task load index; NASA-TLX: National Aeronautics and Space Administration task load index; SWAT: subjective workload assessment technique; DALI: driving activity load index; SURG-TLX: surgery task load index; SIM-TLX: virtual reality simulation task load index; VACP: visual, auditory, cognitive and psychomotor; CVI: content validity index; CVR: content validity ratio; RMSEA: root mean square of error approximation; GFI: goodness of fit index; AGFI: adjusted goodness of fit index; CFI: comparative fit index; ANOVA: analysis of variance; CI: confidence interval.

Profiling cognitive workload in an unmanned vehicle control task with cognitive models and physiological metrics.

In the present study, we use Cognitive Metrics Profiling (CMP) to capture variance in cognitive load within a complex unmanned vehicle control task. We aim to demonstrate convergent validity with existing workload measurement methods, and to decompose workload into constituent cognitive resources to aid in diagnosing causes of workload. A cognitive model of the task was developed and examined to determine the extent to which it could predict behavioral performance, subjective workload, and validated physiological workload metrics. We also examined model activity to draw insights regarding loaded cognitive capacities. We found that composite workload from the model predicted physiological metrics, performance, and subjective workload. Moreover, the model indicates that differences in workload were driven largely by procedural, declarative, and temporal memory demands. We have found preliminary evidence of correspondence between workload predictions of a CMP model and physiological measures of workload. This suggests our approach captures interesting aspects of workload in a complex task environment and may provide a theoretical link between behavioral, physiological, and subjective metrics. This approach may provide a means to design effective workload mitigation interventions and improve decision-making about personnel tasking and automation.

The Measurement of Cognitive Workload in Surgery Using Pupil Metrics: A Systematic Review and Narrative Analysis.

INTRODUCTION: Increased cognitive workload (CWL) is a well-established entity that can impair surgical performance and increase the likelihood of surgical error. The use of pupil and gaze tracking data is increasingly being used to measure CWL objectively in surgery. The aim of this review is to summarize and synthesize the existing evidence that surrounds this. METHODS: A systematic review was undertaken in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A search of OVID MEDLINE, IEEE Xplore, Web of Science, Google Scholar, APA PsychINFO, and EMBASE was conducted for articles published in English between 1990 and January 2021. In total, 6791 articles were screened and 32 full-text articles were selected based on the inclusion criteria. A narrative analysis was undertaken in view of the heterogeneity of studies. RESULTS: Seventy-eight percent of selected studies were deemed high quality. The most frequent surgical environment and task studied was surgical simulation (75%) and performance of laparoscopic skills (56%) respectively. The results demonstrated that the current literature can be broadly categorized into pupil, blink, and gaze metrics used in the assessment of CWL. These can be further categorized according to their use in the context of CWL: (1) direct measurement of CWL (n = 16), (2) determination of expertise level (n = 14), and (3) predictors of performance (n = 2). CONCLUSIONS: Eye-tracking data provide a wealth of information; however, there is marked study heterogeneity. Pupil diameter and gaze entropy demonstrate promise in CWL assessment. Future work will entail the use of artificial intelligence in the form of deep learning and the use of a multisensor platform to accurately measure CWL.

Assessing Cognitive Workload Using Cardiovascular Measures and Voice.

Monitoring cognitive workload has the potential to improve both the performance and fidelity of human decision making. However, previous efforts towards discriminating further than binary levels (e.g., low/high or neutral/high) in cognitive workload classification have not been successful. This lack of sensitivity in cognitive workload measurements might be due to individual differences as well as inadequate methodology used to analyse the measured signal. In this paper, a method that combines the speech signal with cardiovascular measurements for screen and heartbeat classification is introduced. For validation, speech and cardiovascular signals from 97 university participants and 20 airline pilot participants were collected while cognitive stimuli of varying difficulty level were induced with the Stroop colour/word test. For the trinary classification scheme (low, medium, high cognitive workload) the prominent result using classifiers trained on each participant achieved 15.17 ± 0.79% and 17.38 ± 1.85% average misclassification rates indicating good discrimination at three levels of cognitive workload. Combining cardiovascular and speech measures synchronized to each heartbeat and consolidated with short-term dynamic measures might therefore provide enhanced sensitivity in cognitive workload monitoring. The results show that the influence of individual differences is a limiting factor for a generic classification and highlights the need for research to focus on methods that incorporate individual differences to achieve even better results. This method can potentially be used to measure and monitor workload in real time in operational environments.

Quantifying Cognitive Workload Using a Non-Contact Magnetocardiography (MCG) Wearable Sensor.

Quantifying cognitive workload, i.e., the level of mental effort put forth by an individual in response to a cognitive task, is relevant for healthcare, training and gaming applications. However, there is currently no technology available that can readily and reliably quantify the cognitive workload of an individual in a real-world environment at a seamless way and affordable price. In this work, we overcome these limitations and demonstrate the feasibility of a magnetocardiography (MCG) sensor to reliably classify high vs. low cognitive workload while being non-contact, fully passive and low-cost, with the potential to have a wearable form factor. The operating principle relies on measuring the naturally emanated magnetic fields from the heart and subsequently analyzing the heart rate variability (HRV) matrix in three time-domain parameters: standard deviation of RR intervals (SDRR); root mean square of successive differences between heartbeats (RMSSD); and mean values of adjacent R-peaks in the cardiac signals (MeanRR). A total of 13 participants were recruited, two of whom were excluded due to low signal quality. The results show that SDRR and RMSSD achieve a 100% success rate in classifying high vs. low cognitive workload, while MeanRR achieves a 91% success rate. Tests for the same individual yield an intra-subject classification accuracy of 100% for all three HRV parameters. Future studies should leverage machine learning and advanced digital signal processing to achieve automated classification of cognitive workload and reliable operation in a natural environment.