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

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Advances in Human Factors in Robots and Unmanned Systems (AHFE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 962))

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Abstract

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.

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Authors and Affiliations

  1. The University of Alabama, Tuscaloosa, AL, 35401, USA

    Bryan Hernandez-Cuevas, Elijah Sawyers, Landon Bentley & Chris Crawford

  2. University of South Florida, Tampa, FL, 33620, USA

    Marvin Andujar

Authors
  1. Bryan Hernandez-Cuevas
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  2. Elijah Sawyers
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  3. Landon Bentley
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  4. Chris Crawford
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  5. Marvin Andujar
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Corresponding author

Correspondence to Bryan Hernandez-Cuevas .

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Editors and Affiliations

  1. U.S. Army Research Laboratory, Orlando, FL, USA

    Jessie Chen

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Hernandez-Cuevas, B., Sawyers, E., Bentley, L., Crawford, C., Andujar, M. (2020). Neurophysiological Closed-Loop Control for Competitive Multi-brain Robot Interaction. In: Chen, J. (eds) Advances in Human Factors in Robots and Unmanned Systems. AHFE 2019. Advances in Intelligent Systems and Computing, vol 962. Springer, Cham. https://doi.org/10.1007/978-3-030-20467-9_13

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