An online human-agent interaction system: A brain-controlled agent playing games in unity

Cao, Z; Yun, J

An online human-agent interaction system: A brain-controlled agent playing games in unity

Cao, Z

Yun, J

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2021, 3, pp. 1746-1748
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.contributor.author	Yun, J
dc.date	2021-05-03
dc.date.accessioned	2022-06-27T07:39:22Z
dc.date.available	2022-06-27T07:39:22Z
dc.date.issued	2021-01-01
dc.identifier.citation	Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2021, 3, pp. 1746-1748
dc.identifier.isbn	9781713832621
dc.identifier.issn	1548-8403
dc.identifier.issn	1558-2914
dc.identifier.uri	http://hdl.handle.net/10453/158403
dc.description.abstract	Human-agent interactions present people guide an object or agent to act as human intentions. This demonstration work develops an online human-agent interaction system, particularly targeting the brain-computer interface (BCI), which uses real-time brain cortex signals: electroencephalogram (EEG) to control the agent in Unity3D game platform. The developed system also provides the online visualisation of EEG signals, including pre-processed temporal data and power spectral in three frequency bands (theta, alpha, and beta). To build this systematic work, we firstly collect wireless EEG signals via the Bluetooth transmission from a commercially available 14-channel brainware headset (Emotiv). EEG signals are then pre-processed and fed into a trained deep learning model to predict the human intentions, which will be sent to Unity3D platform to control an agent's movements in game playing, such as a karting game scenario. The online testing results show the feasibility of our systematic work that will benefit for human-agent interaction community. The demonstration video can be viewed at the following link: https://youtu.be/9AWKHeatc6I.
dc.language	en
dc.publisher	ACM
dc.relation.ispartof	Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
dc.relation.ispartof	International Conference on Autonomous Agents and MultiAgent Systems
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	An online human-agent interaction system: A brain-controlled agent playing games in unity
dc.type	Conference Proceeding
utslib.citation.volume	3
utslib.location.activity	Virtual Event United Kingdom
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-27T07:39:14Z
pubs.finish-date	2021-05-07
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2021-05-03
pubs.volume	3
dc.location	USA

Abstract:

Human-agent interactions present people guide an object or agent to act as human intentions. This demonstration work develops an online human-agent interaction system, particularly targeting the brain-computer interface (BCI), which uses real-time brain cortex signals: electroencephalogram (EEG) to control the agent in Unity3D game platform. The developed system also provides the online visualisation of EEG signals, including pre-processed temporal data and power spectral in three frequency bands (theta, alpha, and beta). To build this systematic work, we firstly collect wireless EEG signals via the Bluetooth transmission from a commercially available 14-channel brainware headset (Emotiv). EEG signals are then pre-processed and fed into a trained deep learning model to predict the human intentions, which will be sent to Unity3D platform to control an agent's movements in game playing, such as a karting game scenario. The online testing results show the feasibility of our systematic work that will benefit for human-agent interaction community. The demonstration video can be viewed at the following link: https://youtu.be/9AWKHeatc6I.

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http://hdl.handle.net/10453/158403