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International Journal of Fuzzy Logic and Intelligent Systems 2020; 20(4): 261-271

Published online December 25, 2020

https://doi.org/10.5391/IJFIS.2020.20.4.261

© The Korean Institute of Intelligent Systems

Neurological Measurement of Human Trust in Automation Using Electroencephalogram

Seeung Oh1, Younho Seong2, Sun Yi3, and Sangsung Park4

1Department of Applied Engineering Technology at North Carolina Agricultural and Technical State University, Greensboro, USA
2Industrial and systems engineering with North Carolina A&T State University, Greensboro, NC, USA.Greensboro, NC, USA
3Mechanical Engineering at North Carolina A&T State University, USA
4CheongJu University, Cheongju, Korea

Correspondence to :
Seeung Oh (soh1@ncat.edu)

Received: September 10, 2020; Revised: December 2, 2020; Accepted: December 8, 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

In modern society, automation is sufficiently complex to conduct advanced tasks. The role of the human operator in controlling a complex automation is crucial for avoiding failures, reducing risk, and preventing unpredictable situations. Measuring the level of trust of human operators is vital in predicting their acceptance and reliance on automation. In this study, an electroencephalogram (EEG) is used to identify specific brainwaves under trusted and mistrusted cases of automation. A power spectrum analysis was used for a brainwave analysis. The results indicate that the power of the alpha and beta waves is stronger for a trusted situation, whereas the power of gamma waves was stronger for a mistrusted situation. When the level of human trust in automation increases, the use of automatic control increases. Therefore, the findings of this research will contribute to utilizing a neurological technology to measure the level of trust of the human operator, which can affect the decision-making and the overall performance of automation used in industries.

Keywords: Trust, Mistrust, Automation, Electroencephalogram (EEG), Power spectrum

No potential conflict of interest relevant to this article was reported.

Seeung Oh is an adjunct professor in the department of Applied Engineering Technology at North Carolina Agricultural and Technical State University, Greensboro, USA. He earned PhD in Industrial and Systems Engineering at North Carolina Agricultural and Technical State University in 2018. Dr. Oh’s research interests include human factors and ergonomics design with neurological technologies, human trust in automated systems, decision-making and brain-computer interface (BCI) and user interface(UI) design and information visualization.

E-mail: soh1@ncat.edu


Younho Seong received the B.Sc. and M.Sc. degree in industrial engineering from Inha University, South Korea, in 1991 and 1993 respectively, and Ph.D. degree in industrial engineering from State University of New York at Buffalo in 2002. He is currently Professor of industrial and systems engineering with North Carolina A&T State University, Greensboro, NC, USA. His research interests include cognitive engineering, ecological approaches to human judgment & decision-making, machine learning, human trust in automation, social aspect of autonomous vehicles, human–robot interaction, designing interventions to support human cognition, virtual and augmented reality, neuroergonomics, and brain–computer interface.

E-mail: yseong@ncat.edu


Sun Yi is an associate professor of Mechanical Engineering at North Carolina A&T State University. He has developed new and novel methods for sensing and control algorithms for dynamic systems, which are adaptive and robust. The methods have also been applied to networked robots and UAVs/UGVs using AI, neural networks, sensor fusion, machine visions and adaptive control. He has managed research projects supported by DoD, NASA, Dept. Energy, and Dept. Transportation.

E-mail: syi@ncat.edu


Sangsung Park was born in Korea. He received his Master and PhD of Engineering degree in industrial engineering from Korea University. He was research and assistant Professor at Korea University from 2006 to 2018. He is an assistant Professor at the CheongJu University. His research interests are Patent Analysis, Data Mining, Management of Technology, and Technology Evaluation.

E-mail: hanyul@cju.ac.kr


Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2020; 20(4): 261-271

Published online December 25, 2020 https://doi.org/10.5391/IJFIS.2020.20.4.261

Copyright © The Korean Institute of Intelligent Systems.

Neurological Measurement of Human Trust in Automation Using Electroencephalogram

Seeung Oh1, Younho Seong2, Sun Yi3, and Sangsung Park4

1Department of Applied Engineering Technology at North Carolina Agricultural and Technical State University, Greensboro, USA
2Industrial and systems engineering with North Carolina A&T State University, Greensboro, NC, USA.Greensboro, NC, USA
3Mechanical Engineering at North Carolina A&T State University, USA
4CheongJu University, Cheongju, Korea

Correspondence to:Seeung Oh (soh1@ncat.edu)

Received: September 10, 2020; Revised: December 2, 2020; Accepted: December 8, 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In modern society, automation is sufficiently complex to conduct advanced tasks. The role of the human operator in controlling a complex automation is crucial for avoiding failures, reducing risk, and preventing unpredictable situations. Measuring the level of trust of human operators is vital in predicting their acceptance and reliance on automation. In this study, an electroencephalogram (EEG) is used to identify specific brainwaves under trusted and mistrusted cases of automation. A power spectrum analysis was used for a brainwave analysis. The results indicate that the power of the alpha and beta waves is stronger for a trusted situation, whereas the power of gamma waves was stronger for a mistrusted situation. When the level of human trust in automation increases, the use of automatic control increases. Therefore, the findings of this research will contribute to utilizing a neurological technology to measure the level of trust of the human operator, which can affect the decision-making and the overall performance of automation used in industries.

Keywords: Trust, Mistrust, Automation, Electroencephalogram (EEG), Power spectrum

Fig 1.

Figure 1.

International 10–20 system for electrode placement [13].

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 261-271https://doi.org/10.5391/IJFIS.2020.20.4.261

Fig 2.

Figure 2.

Example of EEG experiment.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 261-271https://doi.org/10.5391/IJFIS.2020.20.4.261

Fig 3.

Figure 3.

Example of simulated driving.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 261-271https://doi.org/10.5391/IJFIS.2020.20.4.261

Fig 4.

Figure 4.

Comparison of trust level and use of automatic control.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 261-271https://doi.org/10.5391/IJFIS.2020.20.4.261

Fig 5.

Figure 5.

(a) Comparisons of the intraindividual differences in the alpha waves, (b) comparisons of the intraindividual differences in the beta waves(b), and (c) Comparisons of the intraindividual differences in the gamma waves.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 261-271https://doi.org/10.5391/IJFIS.2020.20.4.261

Fig 6.

Figure 6.

(a) Alpha waves of participants (N=28), (b) beta waves of participants (N=28), and (c) gamma waves of participants (N=28), under states of trust and mistrust, respectively.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 261-271https://doi.org/10.5391/IJFIS.2020.20.4.261

Table 1 . Experiment design.

TrialsTrainingBreak123345678910
Number of cars avoided before an accident2525252525252585498
Auto control performance (%)1001001001001001001003220163632

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