Metrics for individual differences in EEG response to cognitive workload: Optimizing performance prediction

Matthews, Gerald; Reinerman-Jones, Lauren; Abich, Julian; Kustubayeva, Almira

doi:10.1016/j.paid.2017.03.002

Cited by 53 publications

(26 citation statements)

References 21 publications

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“…From the military perspective, stress of this kind may become increasingly significant as Warfighters shift from active combat roles to those that are remote from physical danger such as controlling unmanned vehicles and cyber operations. The greater differentiation of stressor impacts in the subjective data supports previous findings that physiological and subjective indices reflect distinct elements of the stress response, both of which add to evaluation of operator functioning (Matthews et al, 2017b).…”

Section: Discussionsupporting

confidence: 86%

“…Multivariate assessment is important because different responses may have differing functional significance. For example, in a simulation of unmanned ground vehicle operation, Matthews et al (2017b) found that subjective and physiological measures contributed independently to performance prediction; high distress, low heart rate variability, and high frequency EEG were all associated with performance impairment.…”

Section: Introductionmentioning

confidence: 99%

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Metacognition, Hardiness, and Grit as Resilience Factors in Unmanned Aerial Systems (UAS) Operations: A Simulation Study

et al. 2019

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Operators of Unmanned Aerial Systems (UAS) face a variety of stress factors resulting from both the cognitive demands of the work and its broader social context. Dysfunctional metacognitions including those concerning worry may increase stress vulnerability, whereas personality traits including hardiness and grit may confer resilience. The present study utilized a simulation of UAS operation requiring control of multiple vehicles. Two stressors were manipulated independently in a within-subjects design: cognitive demands and negative evaluative feedback. Stress response was assessed using both subjective measures and a suite of psychophysiological sensors, including the electroencephalogram (EEG), electrocardiogram (ECG), and hemodynamic sensors. Both stress manipulations elevated subjective distress and elicited greater high-frequency activity in the EEG. However, predictors of stress response varied across the two stressors. The Anxious Thoughts Inventory (AnTI: Wells , 1994 ) was generally associated with higher state worry in both control and stressor conditions. It also predicted stress reactivity indexed by EEG and worry responses in the negative feedback condition. Measures of hardiness and grit were associated with somewhat different patterns of stress response. In addition, within the negative feedback condition, the AnTI meta-worry scale moderated relationships between state worry and objective performance and psychophysiological outcome measures. Under high state worry, AnTI meta-worry was associated with lower frontal oxygen saturation, but higher spectral power in high-frequency EEG bands. High meta-worry may block adaptive compensatory effort otherwise associated with worry. Findings support both the metacognitive theory of anxiety and negative emotions ( Wells and Matthews, 2015 ), and the Trait-Stressor-Outcome (TSO: Matthews et al., 2017a ) framework for resilience.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Metacognition, Hardiness, and Grit as Resilience Factors in Unmanned Aerial Systems (UAS) Operations: A Simulation Study

et al. 2019

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“…Distress was also associated with greater neglect, irrespective of reliability. Negative relationships between distress and attention have been observed in other multitasking environments (Matthews & Campbell, 2010;Matthews, Reinerman-Jones, et al, 2017) and may reflect disruptive effects of the state on the executive processing necessary to coordinate multiple tasks. Similarly, Eysenck and Derakshan's (2011) Attentional Control Theory acknowledges that anxiety and stress may enhance performance on easy tasks due to compensatory effort.…”

Section: Individual Differences In Stress Response and Performancementioning

confidence: 95%

Vigilance and Automation Dependence in Operation of Multiple Unmanned Aerial Systems (UAS): A Simulation Study

Wohleber¹,

Matthews²,

Lin³

et al. 2018

Hum Factors

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This simulation study investigated factors influencing sustained performance and fatigue during operation of multiple Unmanned Aerial Systems (UAS). The study tested effects of time-on-task and automation reliability on accuracy in surveillance tasks and dependence on automation. It also investigated the role of trait and state individual difference factors. Background: Warm's resource model of vigilance has been highly influential in human factors, but further tests of its applicability to complex, real-world tasks requiring sustained attention are necessary. Multi-UAS operation differs from standard vigilance paradigms in that the operator must switch attention between multiple subtasks, with support from automation. Method: 131 participants performed surveillance tasks requiring signal discrimination and symbol counting with a multi-UAS simulation configured to impose low cognitive demands, for 2 hr. Automation reliability was manipulated between-groups. Five Factor Model personality traits were measured prior to performance. Subjective states were assessed with the Dundee Stress State Questionnaire. Results: Performance accuracy on the more demanding surveillance task showed a vigilance decrement, especially when automation reliability was low. Dependence on automation on this task declined over time. State but not trait factors predicted performance. High distress was associated with poorer performance in more demanding task conditions. Conclusions: Vigilance decrement may be an operational issue for multi-UAS surveillance missions. Warm's resource theory may require modification to incorporate changes in information processing and task strategy associated with multitasking in low-workload, fatiguing environments. Application: Interface design and operator evaluation for multi-UAS operations should address issues including motivation, stress, and sustaining attention to automation.

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“…Notably, transitions from low to high mental demand are associated with a decrease in the alpha band power over the parietal sites and an increase in theta band power over the frontal sites [16,17,18]. Other studies also reported increased beta [19] and gamma bands [20] power associated with higher task demands. Alternatively, time-domain analyses over the EEG signal can also underpin variations of mental workload using passive probe paradigms [21,22].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Pilot’s Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight Conditions

Dehais

Duprès

Blum

et al. 2019

Sensors

124

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Recent technological progress has allowed the development of low-cost and highly portable brain sensors such as pre-amplified dry-electrodes to measure cognitive activity out of the laboratory. This technology opens promising perspectives to monitor the “brain at work” in complex real-life situations such as while operating aircraft. However, there is a need to benchmark these sensors in real operational conditions. We therefore designed a scenario in which twenty-two pilots equipped with a six-dry-electrode EEG system had to perform one low load and one high load traffic pattern along with a passive auditory oddball. In the low load condition, the participants were monitoring the flight handled by a flight instructor, whereas they were flying the aircraft in the high load condition. At the group level, statistical analyses disclosed higher P300 amplitude for the auditory target (Pz, P4 and Oz electrodes) along with higher alpha band power (Pz electrode), and higher theta band power (Oz electrode) in the low load condition as compared to the high load one. Single trial classification accuracy using both event-related potentials and event-related frequency features at the same time did not exceed chance level to discriminate the two load conditions. However, when considering only the frequency features computed over the continuous signal, classification accuracy reached around 70% on average. This study demonstrates the potential of dry-EEG to monitor cognition in a highly ecological and noisy environment, but also reveals that hardware improvement is still needed before it can be used for everyday flight operations.

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Metrics for individual differences in EEG response to cognitive workload: Optimizing performance prediction

Cited by 53 publications

References 21 publications

Metacognition, Hardiness, and Grit as Resilience Factors in Unmanned Aerial Systems (UAS) Operations: A Simulation Study

Metacognition, Hardiness, and Grit as Resilience Factors in Unmanned Aerial Systems (UAS) Operations: A Simulation Study

Vigilance and Automation Dependence in Operation of Multiple Unmanned Aerial Systems (UAS): A Simulation Study

Monitoring Pilot’s Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight Conditions

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