Neurophysiological Closed-Loop Control for Competitive Multi-brain Robot Interaction

This paper discusses a general architecture for multi-party electroencephalography (EEG)-based robot interactions. We explored a system with multiple passive Brain-Computer Interfaces (BCIs) which influence the mechanical behavior of competing mobile robots. Although EEG-based robot control has been previously examined, previous investigations mainly focused on medical applications. Consequently, there is limited work for hybrid control systems that support multi-party, social BCI. Research on multi-user environments, such as gaming, have been conducted to discover challenges for non-medical BCI-based control systems. The presented work aims to provide an architectural model that uses passive BCIs in a social setting including mobile robots. Such structure is comprised of robotic devices able to act intelligently using vision sensors, while receiving and processing EEG data from multiple users. This paper describes the combination of vision sensors, neurophysiological sensors, and modern web technologies to expand knowledge regarding the design of social BCI applications that leverage physical systems.