An EEG Benchmark Data Set for Data-Driven Trust Assessment in Social HRI

Matthias Rehm; Ioannis Pontikis; Giulio Campagna

doi:10.1007/978-981-96-3519-1_33

An EEG Benchmark Data Set for Data-Driven Trust Assessment in Social HRI

Matthias Rehm, Ioannis Pontikis, Giulio Campagna

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

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Abstract

Trust is a crucial element in successful interactions with social robots. However, few existing methods attempt to measure trust in real time to adjust the robot’s behavior according to the user’s trust level. One of the significant challenges in this area is the scarcity of curated data sets necessary for developing machine learning models to detect trust from sensor data. In this paper, we introduce a structured approach to collecting data sets for trust assessment. We demonstrate this approach using an EEG data set from a study designed to build, break, and subsequently repair trust between the user and the robot. The data is available on Zenodo for development of real-time trust assessment models.

Originalsprog	Engelsk
Titel	Social Robotics : 16th International Conference, ICSR + AI 2024, Odense, Denmark, October 23–26, 2024, Proceedings, Part II
Redaktører	Oskar Palinko, Leon Bodenhagen, John-John Cabibihan, Kerstin Fischer, Selma Šabanović, Katie Winkle, Laxmidhar Behera, Shuzhi Sam Ge, Dimitrios Chrysostomou, Wanyue Jiang, Hongsheng He
Antal sider	10
Forlag	Springer
Publikationsdato	2025
Sider	360-369
ISBN (Trykt)	978-981-96-3518-4
ISBN (Elektronisk)	978-981-96-3519-1
DOI	https://doi.org/10.1007/978-981-96-3519-1_33 https://doi.org/10.1007/978-981-96-3519-1_33
Status	Udgivet - 2025
Begivenhed	16th International Conference on Social Robotics, ICSR + AI 2024 - Odense, Danmark Varighed: 23 okt. 2024 → 26 okt. 2024

Konference

Konference	16th International Conference on Social Robotics, ICSR + AI 2024
Land/Område	Danmark
By	Odense
Periode	23/10/2024 → 26/10/2024

Navn	Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence subseries)
ISSN	0302-9743

Bibliografisk note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Verdensmål 9 Industri, innovation og infrastruktur

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Dokument under embargo

Accepted Author Manuscript
Accepteret manuskript, 3,76 MB
Embargo slutter: 25/03/2026

RETRO: Regulating Trust in Human Robot Interaction
Rehm, M. (PI (principal investigator)) & Fischer, K. (PI (principal investigator))
Danmarks Frie Forskningsfond
01/09/2021 → 31/03/2025
Projekter: Projekt › Forskning

Benchmark EEG data set for trust assessment for interactions with social robots
Rehm, M. (Ophavsperson) & Campagna, G. (Ophavsperson), Zenodo, 21 aug. 2024
DOI: 10.5281/zenodo.12754928
Datasæt

Citationsformater

Rehm, M., Pontikis, I., & Campagna, G. (2025). An EEG Benchmark Data Set for Data-Driven Trust Assessment in Social HRI. I O. Palinko, L. Bodenhagen, J.-J. Cabibihan, K. Fischer, S. Šabanović, K. Winkle, L. Behera, S. S. Ge, D. Chrysostomou, W. Jiang, & H. He (red.), Social Robotics: 16th International Conference, ICSR + AI 2024, Odense, Denmark, October 23–26, 2024, Proceedings, Part II (s. 360-369). Springer. https://doi.org/10.1007/978-981-96-3519-1_33, https://doi.org/10.1007/978-981-96-3519-1_33

Rehm, Matthias ; Pontikis, Ioannis ; Campagna, Giulio. / An EEG Benchmark Data Set for Data-Driven Trust Assessment in Social HRI. Social Robotics: 16th International Conference, ICSR + AI 2024, Odense, Denmark, October 23–26, 2024, Proceedings, Part II. red. / Oskar Palinko ; Leon Bodenhagen ; John-John Cabibihan ; Kerstin Fischer ; Selma Šabanović ; Katie Winkle ; Laxmidhar Behera ; Shuzhi Sam Ge ; Dimitrios Chrysostomou ; Wanyue Jiang ; Hongsheng He. Springer, 2025. s. 360-369 (Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence subseries)).

@inproceedings{e562f710095b4bef97834e4a996b1f3f,

title = "An EEG Benchmark Data Set for Data-Driven Trust Assessment in Social HRI",

abstract = "Trust is a crucial element in successful interactions with social robots. However, few existing methods attempt to measure trust in real time to adjust the robot{\textquoteright}s behavior according to the user{\textquoteright}s trust level. One of the significant challenges in this area is the scarcity of curated data sets necessary for developing machine learning models to detect trust from sensor data. In this paper, we introduce a structured approach to collecting data sets for trust assessment. We demonstrate this approach using an EEG data set from a study designed to build, break, and subsequently repair trust between the user and the robot. The data is available on Zenodo for development of real-time trust assessment models.",

keywords = "AI, Human robot interaction, Machine learning, Social Robotics, human robot trust, Dataset, Human Robot Trust, Machine Learning",

author = "Matthias Rehm and Ioannis Pontikis and Giulio Campagna",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 16th International Conference on Social Robotics, ICSR + AI 2024, ICSR{\textquoteright}24 ; Conference date: 23-10-2024 Through 26-10-2024",

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doi = "10.1007/978-981-96-3519-1\_33",

language = "English",

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pages = "360--369",

editor = "Oskar Palinko and Leon Bodenhagen and John-John Cabibihan and Kerstin Fischer and Selma {\v S}abanovi{\'c} and Katie Winkle and Laxmidhar Behera and Ge, \{Shuzhi Sam\} and Dimitrios Chrysostomou and Wanyue Jiang and Hongsheng He",

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Rehm, M, Pontikis, I & Campagna, G 2025, An EEG Benchmark Data Set for Data-Driven Trust Assessment in Social HRI. i O Palinko, L Bodenhagen, J-J Cabibihan, K Fischer, S Šabanović, K Winkle, L Behera, SS Ge, D Chrysostomou, W Jiang & H He (red), Social Robotics: 16th International Conference, ICSR + AI 2024, Odense, Denmark, October 23–26, 2024, Proceedings, Part II. Springer, Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence subseries), s. 360-369, 16th International Conference on Social Robotics, ICSR + AI 2024, Odense, Danmark, 23/10/2024. https://doi.org/10.1007/978-981-96-3519-1_33, https://doi.org/10.1007/978-981-96-3519-1_33

An EEG Benchmark Data Set for Data-Driven Trust Assessment in Social HRI. / Rehm, Matthias; Pontikis, Ioannis; Campagna, Giulio.
Social Robotics: 16th International Conference, ICSR + AI 2024, Odense, Denmark, October 23–26, 2024, Proceedings, Part II. red. / Oskar Palinko; Leon Bodenhagen; John-John Cabibihan; Kerstin Fischer; Selma Šabanović; Katie Winkle; Laxmidhar Behera; Shuzhi Sam Ge; Dimitrios Chrysostomou; Wanyue Jiang; Hongsheng He. Springer, 2025. s. 360-369 (Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence subseries)).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - An EEG Benchmark Data Set for Data-Driven Trust Assessment in Social HRI

AU - Rehm, Matthias

AU - Pontikis, Ioannis

AU - Campagna, Giulio

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - Trust is a crucial element in successful interactions with social robots. However, few existing methods attempt to measure trust in real time to adjust the robot’s behavior according to the user’s trust level. One of the significant challenges in this area is the scarcity of curated data sets necessary for developing machine learning models to detect trust from sensor data. In this paper, we introduce a structured approach to collecting data sets for trust assessment. We demonstrate this approach using an EEG data set from a study designed to build, break, and subsequently repair trust between the user and the robot. The data is available on Zenodo for development of real-time trust assessment models.

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KW - AI

KW - Human robot interaction

KW - Machine learning

KW - Social Robotics

KW - human robot trust

KW - Dataset

KW - Human Robot Trust

KW - Machine Learning

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U2 - 10.1007/978-981-96-3519-1_33

DO - 10.1007/978-981-96-3519-1_33

M3 - Article in proceeding

AN - SCOPUS:105002159499

SN - 978-981-96-3518-4

T3 - Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence subseries)

SP - 360

EP - 369

BT - Social Robotics

A2 - Palinko, Oskar

A2 - Bodenhagen, Leon

A2 - Cabibihan, John-John

A2 - Fischer, Kerstin

A2 - Šabanović, Selma

A2 - Winkle, Katie

A2 - Behera, Laxmidhar

A2 - Ge, Shuzhi Sam

A2 - Chrysostomou, Dimitrios

A2 - Jiang, Wanyue

A2 - He, Hongsheng

PB - Springer

T2 - 16th International Conference on Social Robotics, ICSR + AI 2024

Y2 - 23 October 2024 through 26 October 2024

ER -

Rehm M, Pontikis I, Campagna G. An EEG Benchmark Data Set for Data-Driven Trust Assessment in Social HRI. I Palinko O, Bodenhagen L, Cabibihan JJ, Fischer K, Šabanović S, Winkle K, Behera L, Ge SS, Chrysostomou D, Jiang W, He H, red., Social Robotics: 16th International Conference, ICSR + AI 2024, Odense, Denmark, October 23–26, 2024, Proceedings, Part II. Springer. 2025. s. 360-369. (Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence subseries)). doi: 10.1007/978-981-96-3519-1_33, 10.1007/978-981-96-3519-1_33

An EEG Benchmark Data Set for Data-Driven Trust Assessment in Social HRI

Abstract

Konference

Bibliografisk note

FN’s Verdensmål

Adgang til dokumentet

AUB Link

Andre filer og links

Dokument under embargo

Fingeraftryk

Projekter

RETRO: Regulating Trust in Human Robot Interaction

Forskningsdatasæt

Benchmark EEG data set for trust assessment for interactions with social robots

Citationsformater