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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

Item Type:	Refereed Conference Paper
Keywords:	EEG, brain, agent, interaction, human-agent interaction, brain-computer interface, unity game
Research Division:	Information and Computing Sciences
Research Group:	Artificial intelligence
Research Field:	Autonomous agents and multiagent systems
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Artificial intelligence
UTAS Author:	Cao, Z (Dr Zehong Cao)
UTAS Author:	Yun, J ( Jie Yun)
ID Code:	142991
Year Published:	In Press
Deposited By:	Information and Communication Technology
Deposited On:	2021-02-19
Last Modified:	2021-04-07
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