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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.

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During sleep deprivation, neurobehavioral functions requiring sustained levels of attention and alertness are significantly impaired. Discrepancies between subjective measures of sleepiness and objective performance during sustained operations have led to interest in physiological monitoring of operator performance. Alertness, vigilance, and arousal are modulated by the wake-promoting actions of the central noradrenergic system. Salivary alpha-amylase (sAA) has been proposed as a sensitive peripheral measure of noradrenergic activity, but limited research has investigated the relationship between sAA and performance. In a laboratory-controlled environment, we investigated the relationship between sAA levels, subjective sleepiness, and performance during two days (50h) of total sleep deprivation. Beginning at 09:00, twelve healthy participants (5 females) aged 22.5±2.5years (mean±SD) provided saliva samples, recorded ratings of subjective sleepiness, completed a brief 3-min psychomotor vigilance task (PVT-B) and performed a 40-min simulated driving task, at regular 3h intervals during wakefulness. Ratings of subjective sleepiness exhibited a constant linear increase (p<0.001) during sleep deprivation. In contrast, sAA levels showed a marked diurnal profile, with levels increasing during the day (p<0.001) and steadily declining in the evening and early-morning (p<0.001). PVT-B (mean reaction time and mean slowest 10% reaction time) and simulated driving performance (speed deviation and lane deviation) also exhibited diurnal profiles across the two days of sleep deprivation. Performance peaked in the afternoon (p<0.001) and then steadily worsened as wakefulness continued into the evening and early-morning (p<0.001). Further analysis revealed that higher sAA levels in the hour preceding each performance assessment were associated with better PVT-B and driving performance (p<0.001). These findings suggest that sAA measures may be suitable indicators of performance deficits during sustained wakefulness and highlight the potential for sAA to be considered for physiological monitoring of performance. In operational environments sAA levels, as part of a panel of physiological measures, may be useful for assessing fitness-for-duty prior to safety being compromised or when performance deficits are unknown.