TY - JOUR
T1 - Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments
AU - Fernandez Rojas, Raul
AU - Debie, Essam
AU - Fidock, Justin
AU - Barlow, Michael
AU - Kasmarik, Kathryn
AU - Anavatti, Sreenatha
AU - Garratt, Matthew
AU - Abbass, Hussein
N1 - Funding Information:
We thank D. Flück and M. Grob for assistance in the field, the local farmers for permission to work on their land and three anonymous reviewers for their helpful comments on an earlier version of the manuscript. This work was supported by the Swiss National Science Foundation [grant number 31003A-127246 ].
Publisher Copyright:
© Copyright © 2020 Fernandez Rojas, Debie, Fidock, Barlow, Kasmarik, Anavatti, Garratt and Abbass.
Funding Information:
The Commonwealth of Australia supported this research through the Australian Army and a Defence Science Partnerships agreement with the Defence Science and Technology Group, as part of the Human Performance Research Network.
Publisher Copyright:
© Copyright © 2020 Fernandez Rojas, Debie, Fidock, Barlow, Kasmarik, Anavatti, Garratt and Abbass.
PY - 2020/2/14
Y1 - 2020/2/14
N2 - 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.
AB - 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.
KW - augmented intelligence
KW - cognitive indicators
KW - cognitive load
KW - EEG
KW - human-autonomy teaming
KW - human-swarm teaming
KW - mental load
KW - shepherding
UR - http://www.scopus.com/inward/record.url?scp=85080038605&partnerID=8YFLogxK
U2 - 10.3389/fnins.2020.00040
DO - 10.3389/fnins.2020.00040
M3 - Article
C2 - 32116498
AN - SCOPUS:85080038605
SN - 1662-4548
VL - 14
SP - 1
EP - 15
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
M1 - 40
ER -