Extracting Human Levels of Trust in Human–Swarm Interaction Using EEG Signals

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Author(s)Orozco, Jesus A.
Author(s)Artemiadis, Panagiotis
Date Accessioned2024-05-02T18:18:25Z
Date Available2024-05-02T18:18:25Z
Publication Date2024-02-07
DescriptionThis article was originally published in IEEE Transactions on Human-Machine Systems. The version of record is available at: https://doi.org/10.1109/THMS.2024.3356421. © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. See https://www.ieee.org/publications/rights/index.html for more information. This article will be embargoed until 02/07/2026.
AbstractTrust is an essential building block of human civilization. However, when it relates to artificial systems, it has been a barrier to intelligent technology adoption in general. This article addresses the gap in determining levels of trust in scenarios that include humans interacting with a swarm of robots. Electroencephalography (EEG) recordings of the human observers of the different swarms allow for extracting specific EEG features related to different trust levels. Feature selection and machine learning methods comprise a classification system that would allow recognition of different levels of human trust in those human–swarm interaction scenarios. The results of this study suggest that EEG correlates of swarm trust exist and are distinguishable in machine learning feature classification with very high accuracy. Moreover, comparing common EEG features across all human subjects used in this study allows for the generalization of the classification method, providing solid evidence of specific areas and features of the human brain where activations are related to levels of human–swarm trust. This work has direct implications for effective human–machine teaming with applications to many fields, such as exploration, search and rescue operations, surveillance, environmental monitoring, and defense. In these applications, quantifying levels of human trust in the deployed swarm is of utmost importance because it can lead to swarm controllers that adapt their output based on the human's perceived trust level.
SponsorThis material is based upon work supported by the National Science Foundation under Grant No. #2014264, as well as by the U.S. Air Force Office of Scientific Research (AFOSR) award FA9550-18-1-0221.
CitationJ. A. Orozco and P. Artemiadis, "Extracting Human Levels of Trust in Human–Swarm Interaction Using EEG Signals," in IEEE Transactions on Human-Machine Systems, vol. 54, no. 2, pp. 182-191, April 2024, doi: 10.1109/THMS.2024.3356421
ISSN2168-2305
URLhttps://udspace.udel.edu/handle/19716/34335
Languageen_US
PublisherIEEE Transactions on Human-Machine Systems
Keywordselectroencephalography (EEG)
Keywordshuman–swarm interaction (HSI)
Keywordstrust
TitleExtracting Human Levels of Trust in Human–Swarm Interaction Using EEG Signals
TypeArticle
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