Cargando…

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Fernandez Rojas, Raul, Debie, Essam, Fidock, Justin, Barlow, Michael, Kasmarik, Kathryn, Anavatti, Sreenatha, Garratt, Matthew, Abbass, Hussein
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034033/
https://www.ncbi.nlm.nih.gov/pubmed/32116498
http://dx.doi.org/10.3389/fnins.2020.00040
_version_ 1783499796862992384
author Fernandez Rojas, Raul
Debie, Essam
Fidock, Justin
Barlow, Michael
Kasmarik, Kathryn
Anavatti, Sreenatha
Garratt, Matthew
Abbass, Hussein
author_facet Fernandez Rojas, Raul
Debie, Essam
Fidock, Justin
Barlow, Michael
Kasmarik, Kathryn
Anavatti, Sreenatha
Garratt, Matthew
Abbass, Hussein
author_sort Fernandez Rojas, Raul
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7034033
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-70340332020-02-28 Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments Fernandez Rojas, Raul Debie, Essam Fidock, Justin Barlow, Michael Kasmarik, Kathryn Anavatti, Sreenatha Garratt, Matthew Abbass, Hussein Front Neurosci Neuroscience 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. Frontiers Media S.A. 2020-02-14 /pmc/articles/PMC7034033/ /pubmed/32116498 http://dx.doi.org/10.3389/fnins.2020.00040 Text en Copyright © 2020 Fernandez Rojas, Debie, Fidock, Barlow, Kasmarik, Anavatti, Garratt and Abbass. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Fernandez Rojas, Raul
Debie, Essam
Fidock, Justin
Barlow, Michael
Kasmarik, Kathryn
Anavatti, Sreenatha
Garratt, Matthew
Abbass, Hussein
Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments
title Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments
title_full Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments
title_fullStr Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments
title_full_unstemmed Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments
title_short Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments
title_sort electroencephalographic workload indicators during teleoperation of an unmanned aerial vehicle shepherding a swarm of unmanned ground vehicles in contested environments
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034033/
https://www.ncbi.nlm.nih.gov/pubmed/32116498
http://dx.doi.org/10.3389/fnins.2020.00040
work_keys_str_mv AT fernandezrojasraul electroencephalographicworkloadindicatorsduringteleoperationofanunmannedaerialvehicleshepherdingaswarmofunmannedgroundvehiclesincontestedenvironments
AT debieessam electroencephalographicworkloadindicatorsduringteleoperationofanunmannedaerialvehicleshepherdingaswarmofunmannedgroundvehiclesincontestedenvironments
AT fidockjustin electroencephalographicworkloadindicatorsduringteleoperationofanunmannedaerialvehicleshepherdingaswarmofunmannedgroundvehiclesincontestedenvironments
AT barlowmichael electroencephalographicworkloadindicatorsduringteleoperationofanunmannedaerialvehicleshepherdingaswarmofunmannedgroundvehiclesincontestedenvironments
AT kasmarikkathryn electroencephalographicworkloadindicatorsduringteleoperationofanunmannedaerialvehicleshepherdingaswarmofunmannedgroundvehiclesincontestedenvironments
AT anavattisreenatha electroencephalographicworkloadindicatorsduringteleoperationofanunmannedaerialvehicleshepherdingaswarmofunmannedgroundvehiclesincontestedenvironments
AT garrattmatthew electroencephalographicworkloadindicatorsduringteleoperationofanunmannedaerialvehicleshepherdingaswarmofunmannedgroundvehiclesincontestedenvironments
AT abbasshussein electroencephalographicworkloadindicatorsduringteleoperationofanunmannedaerialvehicleshepherdingaswarmofunmannedgroundvehiclesincontestedenvironments