Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments
| Autor: | Raul Fernandez Rojas, Sreenatha G. Anavatti, Essam Debie, Matthew Garratt, Michael Barlow, Justin Fidock, Hussein A. Abbass, Kathryn Kasmarik |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
shepherding mental load Electroencephalography Machine learning computer.software_genre 050105 experimental psychology lcsh:RC321-571 03 medical and health sciences 0302 clinical medicine medicine Human multitasking 0501 psychology and cognitive sciences EEG Set (psychology) lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Original Research human-swarm teaming cognitive indicators human-autonomy teaming augmented intelligence medicine.diagnostic_test business.industry cognitive load General Neuroscience 05 social sciences Swarm behaviour Workload Teleoperation Artificial intelligence business computer 030217 neurology & neurosurgery Augmented cognition Cognitive load Neuroscience |
| Zdroj: | Frontiers in Neuroscience Frontiers in Neuroscience, Vol 14 (2020) |
| ISSN: | 1662-453X |
| DOI: | 10.3389/fnins.2020.00040 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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