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Brain Art pp 309-323 | Cite as

Self-conscience/Physical Memory: An Immersive, Kinetic Art Installation Driven by Real-Time and Archival EEG Signals

  • Eric ToddEmail author
  • Jesus G. Cruz-Garza
  • Austin Moreau
  • James Templeton
  • Jose Luis Contreras-Vidal
Chapter

Abstract

The intermingling of art and science has often been seen as equivocal, as noncommittal, the art world dubious of the certainty of science and science seeking function in art, but both disciplines very often are in search of the same thing, something we can generalize, something common among us. Neuroscience in particular seeks to give definition to questions that art and poetry, mysticism, religion, and psychology have been asking since the beginning of recorded history and presumably longer. These are fundamental questions about the nature of identity, ideas we’ve named and redefined many times over. Self-conscience/Physical Memory is a brain-controlled robotic sculpture in the University of Houston’s Noninvasive Brain Machine Interface Laboratory. Motorized and illuminated acrylic ceiling tiles shift the architecture of the space itself in response to EEG data. The height of the panels is driven by alpha power suppression in the central cortical areas, and the tiles’ color shifts with alpha power changes in the occipital and frontal lobes. The EEG data can be input in real-time by a single participant, or, in the absence of user input, the work also serves as a playback device for archival EEG recordings, a physical manifestation of a past experience, of a moment in someone’s life, a person both absent and present in that new moment.

Keywords

Installation EEG Neuroscience Kinetic sculpture Brain-computer interface 

Notes

Acknowledgements

This work was partly funded by the University of Houston Cullen College of Engineering, NSF Awards 1219321 and 1302339, NSF NCS-FO 1533691, NSF Award #1650566 I/UCRC BRAIN Center, NSF I/UCRC BRAIN Award CNS1650536, NSF REU Site Award #EEC-1757949, and the University of Houston’s 2017 SURF program.

We would also like to personally thank Atilla Killacarslan, Yongtian He, Andrew Paek, and Daniel Schaeffer for their patient consultation and unwavering support.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Eric Todd
    • 1
    Email author
  • Jesus G. Cruz-Garza
    • 1
  • Austin Moreau
    • 1
  • James Templeton
    • 1
  • Jose Luis Contreras-Vidal
    • 1
  1. 1.Noninvasive Brain-Machine Interface Systems Lab, Department of Electrical & Computer EngineeringCullen Engineering Building II, University of HoustonHoustonUSA

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