EEG-Based Neural Correlates of Trust in Human-Autonomy Interaction

Min Wang; Aya Hussein; Raul Fernandez Rojas; Kamran Shafi; Hussein A. Abbass

doi:10.1109/SSCI.2018.8628649

EEG-Based Neural Correlates of Trust in Human-Autonomy Interaction

Min Wang, Aya Hussein, Raul Fernandez Rojas, Kamran Shafi, Hussein A. Abbass

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

22 Citations (Scopus)

Abstract

This paper aims at identifying the neural correlates of human trust in autonomous systems using Electroencephalography (EEG) signals. Quantifying the relationship between trust and brain activities allows for real-time assessment of human trust in automation. This line of effort contributes to the design of trusted autonomous systems, and more generally, modeling the interaction in human-autonomy interaction. To study the correlates of trust, we use an investment game in which artificial agents with different levels of trustworthiness are employed. We collected EEG signals from 10 human subjects while they are playing the game; then computed three types of features from these signals considering the signal time-dependency, complexity and power spectrum using an autoregressive model (AR), sample entropy and Fourier analysis, respectively. Results of a mixed model analysis showed significant correlation ( p <0.05) between human trust and EEG features from certain electrodes. The frontal and the occipital area are identified as the predominant brain areas correlated with trust.

Original language	English
Title of host publication	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
Editors	Nikhil R Pal, Suresh Sundaram
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	350-357
Number of pages	8
ISBN (Electronic)	9781538692769
ISBN (Print)	9781538692776
DOIs	https://doi.org/10.1109/SSCI.2018.8628649
Publication status	Published - 28 Jan 2019
Externally published	Yes
Event	2018 IEEE Symposium Series on Computational Intelligence - Sheraton Grand Bangalore Hotel, Bengalore, India Duration: 18 Nov 2018 → 21 Nov 2018 http://ieee-ssci2018.org/

Publication series

Name	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

Conference

Conference	2018 IEEE Symposium Series on Computational Intelligence
Abbreviated title	IEEE-SSCI 2018
Country/Territory	India
City	Bengalore
Period	18/11/18 → 21/11/18
Internet address	http://ieee-ssci2018.org/

Access to Document

10.1109/SSCI.2018.8628649

Cite this

Wang, M., Hussein, A., Rojas, R. F., Shafi, K., & Abbass, H. A. (2019). EEG-Based Neural Correlates of Trust in Human-Autonomy Interaction. In N. R. Pal, & S. Sundaram (Eds.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 350-357). [8628649] (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SSCI.2018.8628649

Wang, Min ; Hussein, Aya ; Rojas, Raul Fernandez et al. / EEG-Based Neural Correlates of Trust in Human-Autonomy Interaction. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. editor / Nikhil R Pal ; Suresh Sundaram. United States : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 350-357 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

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title = "EEG-Based Neural Correlates of Trust in Human-Autonomy Interaction",

abstract = "This paper aims at identifying the neural correlates of human trust in autonomous systems using Electroencephalography (EEG) signals. Quantifying the relationship between trust and brain activities allows for real-time assessment of human trust in automation. This line of effort contributes to the design of trusted autonomous systems, and more generally, modeling the interaction in human-autonomy interaction. To study the correlates of trust, we use an investment game in which artificial agents with different levels of trustworthiness are employed. We collected EEG signals from 10 human subjects while they are playing the game; then computed three types of features from these signals considering the signal time-dependency, complexity and power spectrum using an autoregressive model (AR), sample entropy and Fourier analysis, respectively. Results of a mixed model analysis showed significant correlation ( p <0.05) between human trust and EEG features from certain electrodes. The frontal and the occipital area are identified as the predominant brain areas correlated with trust.",

keywords = "EEG, Human factor, Human-autonomy interaction, Mixed model analysis, Trust",

author = "Min Wang and Aya Hussein and Rojas, {Raul Fernandez} and Kamran Shafi and Abbass, {Hussein A.}",

note = "Funding Information: The study was conducted after approval from UNSW Human Research Ethics Compliance Committee Protocol HC17434. This work was funded by the Australian Research Council Discovery Grant number DP160102037. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Symposium Series on Computational Intelligence, IEEE-SSCI 2018 ; Conference date: 18-11-2018 Through 21-11-2018",

year = "2019",

month = jan,

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doi = "10.1109/SSCI.2018.8628649",

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Wang, M, Hussein, A, Rojas, RF, Shafi, K & Abbass, HA 2019, EEG-Based Neural Correlates of Trust in Human-Autonomy Interaction. in NR Pal & S Sundaram (eds), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018., 8628649, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 350-357, 2018 IEEE Symposium Series on Computational Intelligence, Bengalore, India, 18/11/18. https://doi.org/10.1109/SSCI.2018.8628649

EEG-Based Neural Correlates of Trust in Human-Autonomy Interaction. / Wang, Min; Hussein, Aya; Rojas, Raul Fernandez et al.
Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. ed. / Nikhil R Pal; Suresh Sundaram. United States: IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 350-357 8628649 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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AU - Hussein, Aya

AU - Rojas, Raul Fernandez

AU - Shafi, Kamran

AU - Abbass, Hussein A.

N1 - Funding Information: The study was conducted after approval from UNSW Human Research Ethics Compliance Committee Protocol HC17434. This work was funded by the Australian Research Council Discovery Grant number DP160102037. Publisher Copyright: © 2018 IEEE.

PY - 2019/1/28

Y1 - 2019/1/28

N2 - This paper aims at identifying the neural correlates of human trust in autonomous systems using Electroencephalography (EEG) signals. Quantifying the relationship between trust and brain activities allows for real-time assessment of human trust in automation. This line of effort contributes to the design of trusted autonomous systems, and more generally, modeling the interaction in human-autonomy interaction. To study the correlates of trust, we use an investment game in which artificial agents with different levels of trustworthiness are employed. We collected EEG signals from 10 human subjects while they are playing the game; then computed three types of features from these signals considering the signal time-dependency, complexity and power spectrum using an autoregressive model (AR), sample entropy and Fourier analysis, respectively. Results of a mixed model analysis showed significant correlation ( p <0.05) between human trust and EEG features from certain electrodes. The frontal and the occipital area are identified as the predominant brain areas correlated with trust.

AB - This paper aims at identifying the neural correlates of human trust in autonomous systems using Electroencephalography (EEG) signals. Quantifying the relationship between trust and brain activities allows for real-time assessment of human trust in automation. This line of effort contributes to the design of trusted autonomous systems, and more generally, modeling the interaction in human-autonomy interaction. To study the correlates of trust, we use an investment game in which artificial agents with different levels of trustworthiness are employed. We collected EEG signals from 10 human subjects while they are playing the game; then computed three types of features from these signals considering the signal time-dependency, complexity and power spectrum using an autoregressive model (AR), sample entropy and Fourier analysis, respectively. Results of a mixed model analysis showed significant correlation ( p <0.05) between human trust and EEG features from certain electrodes. The frontal and the occipital area are identified as the predominant brain areas correlated with trust.

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M3 - Conference contribution

SN - 9781538692776

T3 - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

SP - 350

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BT - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

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Y2 - 18 November 2018 through 21 November 2018

ER -

Wang M, Hussein A, Rojas RF, Shafi K, Abbass HA. EEG-Based Neural Correlates of Trust in Human-Autonomy Interaction. In Pal NR, Sundaram S, editors, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. United States: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 350-357. 8628649. (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). doi: 10.1109/SSCI.2018.8628649

EEG-Based Neural Correlates of Trust in Human-Autonomy Interaction

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