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Exploring Audience Response in Performing Arts with a Brain-Adaptive Digital Performance System

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Published:04 December 2017Publication History
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Abstract

Audience response is an important indicator of the quality of performing arts. Psychophysiological measurements enable researchers to perceive and understand audience response by collecting their bio-signals during a live performance. However, how the audience respond and how the performance is affected by these responses are the key elements but are hard to implement. To address this issue, we designed a brain-computer interactive system called Brain-Adaptive Digital Performance (BADP) for the measurement and analysis of audience engagement level through an interactive three-dimensional virtual theater. The BADP system monitors audience engagement in real time using electroencephalography (EEG) measurement and tries to improve it by applying content-related performing cues when the engagement level decreased.

In this article, we generate EEG-based engagement level and build thresholds to determine the decrease and re-engage moments. In the experiment, we simulated two types of theatre performance to provide participants a high-fidelity virtual environment using the BADP system. We also create content-related performing cues for each performance under three different conditions. The results of these evaluations show that our algorithm could accurately detect the engagement status and the performing cues have a positive impact on regaining audience engagement across different performance types. Our findings open new perspectives in audience-based theatre performance design.

References

  1. Jennifer Radbourne, Hilary Glow, and Katya Johanson. 2010. Hidden stories: Listening to the audience at the live performance. Double Dialogues 13 (2010), 1--14.Google ScholarGoogle Scholar
  2. Jennifer Radbourne, Katya Johanson, Hilary Glow, and Tabitha White. 2009. The audience experience: Measuring quality in the performing arts. International Journal of Arts Management 11, 16--29.Google ScholarGoogle Scholar
  3. Inma Álvarez, Héctor J. Pérez, and Francisca Pérez-Carreño. 2010. Expression in the Performing Arts. Cambridge Scholars Publishing.Google ScholarGoogle Scholar
  4. Giulio Jacucci, Stephen Fairclough, and Erin T. Solovey. 2015. Physiological computing. Computer 48, 10 (2015), 12--16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Gabor Aranyi, Fred Charles, and Marc Cavazza. 2015. Anger-based BCI Using fNIRS Neurofeedback. In Proceedings of the 28th Annual ACM Symposium on User Interface Software 8 Technology. 511--521. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Gabor Aranyi, Florian Pecune, Fred Charles, Catherine Pelachaud, and Marc Cavazza. 2016. Affective interaction with a virtual character through an fNIRS brain-computer interface. Frontiers in Computational Neuroscience 10 (2016), 70.Google ScholarGoogle ScholarCross RefCross Ref
  7. T. O. Zander and C. Kothe. 2011. Towards passive brain--computer interfaces: Applying brain--computer interface technology to human--machine systems in general. Journal of Neural Engineering.Google ScholarGoogle ScholarCross RefCross Ref
  8. L. George and A. Lécuyer. 2010. An overview of research on “passive” brain-computer interfaces for implicit human-computer interaction. In International Conference on Applied Bionics and Biomechanics (ICABB’10) -- Workshop W1 “Brain-Computer Interfacing and Virtual Reality.”Google ScholarGoogle Scholar
  9. Louise Barkhuus and Chiara Rossitto. 2016. Acting with technology: Rehearsing for mixed-media live performances. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 864--875. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Steve Dixon. 2007. Digital Performance: A History of New Media in Theater, Dance, Performance Art, and Installation. MIT Press.Google ScholarGoogle ScholarCross RefCross Ref
  11. Christa Williford. 2000. A computer reconstruction of Richelieu's Palais Cardinal theatre, 1641. Theatre Research International 25, 3 (2000), 233--247.Google ScholarGoogle ScholarCross RefCross Ref
  12. David Z. Saltz. 2008. Performing arts. In A Companion to Digital Humanities, Susan Schreibman, Ray Siemens, and John Unsworth (Ed.). John Wiley 8 Sons Press, 121--131.Google ScholarGoogle Scholar
  13. Thomas B. Moeslund, Adrian Hilton, and Volker Krüger. 2006. A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104, 2 (2006), 90--126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. David Z. Saltz. 2004. Virtual Vaudeville. http://www.virtualvaudeville.com/index.htm.Google ScholarGoogle Scholar
  15. Peter Dalsgaard and Lone Koefoed Hansen. 2008. Performing perception—Staging aesthetics of interaction. ACM Transactions on Computer-Human Interaction (TOCHI) 15, 3 (2008), 13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Stuart Reeves. 2011. Designing Interfaces in Public Settings: Understanding the Role of the Spectator in Human-Computer Interaction. Springer Science 8 Business Media. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Steve Benford and Gabriella Giannachi. 2011. Performing Mixed Reality. The MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Jacucci Giulio. 2015. Interaction as performance: Performative strategies in designing interactive experiences. Ubiquitous Computing, Complexity, and Culture. Routledge, New York.Google ScholarGoogle Scholar
  19. Sander Koelstra, Christian Muhl, Mohammad Soleymani, Jong-Seok Lee, Ashkan Yazdani, and Touradj Ebrahimi. 2012. DEAP: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing 3, 1 (2012), 18--31. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Mojtaba Khomami Abadi, Ramanathan Subramanian, Seyed Mostafa Kia, Paolo Avesani, Ioannis Patras, and Nicu Sebe. 2015. DECAF: MEG-based multimodal database for decoding affective physiological responses. IEEE Transactions on Affective Computing 6, 3 (2015), 209--222.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Giulio Jacucci, Anna Spagnolli, Alessandro Chalambalakis, Ann Morrison, Lassi Liikkanen, Stefano Roveda, and Massimo Bertoncini. 2009. Bodily explorations in space: Social experience of a multimodal art installation. In Proceedings of the IFIP Conference on Human-Computer Interaction. 62--75. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Celine Latulipe, Erin A. Carroll, and Danielle Lottridge. 2011. Love, hate, arousal and engagement: Exploring audience responses to performing arts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1845--1854. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Peter J. Lang. 1995. The emotion probe: Studies of motivation and attention. American Psychologist 50, 5.Google ScholarGoogle ScholarCross RefCross Ref
  24. Wolfram Boucsein. 2012. Electrodermal Activity. Springer Science 8 Business Media.Google ScholarGoogle Scholar
  25. Chen Wang, Erik N. Geelhoed, Phil P. Stenton, and Pablo Cesar. 2014. Sensing a live audience. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems. 1909--1912. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Beatriz Calvo-Merino, Corinne Jola, Daniel E. Glaser, and Patrick Haggard. 2008. Towards a sensorimotor aesthetics of performing art. Consciousness and Cognition 17, 3 (2008), 911--922.Google ScholarGoogle ScholarCross RefCross Ref
  27. Desney Tan and Anton Nijholt. 2010. Brain-Computer Interface: Applying Our Minds to Human-Computer Interaction. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Pia Tikka, Rasmus Vuori, and Mauri Kaipainen. 2006. Narrative logic of enactive cinema: Obsession. Digital Creativity 17, 4, 205--212.Google ScholarGoogle ScholarCross RefCross Ref
  29. Mauri Kaipainen, Niklas Ravaja, Pia Tikka, Rasmus Vuori, Roberto Pugliese, Marco Rapino, and Tapio Takala. 2011. Enactive systems and enactive media: Embodied human-machine coupling beyond interfaces. Leonardo 44, 5, 433--438.Google ScholarGoogle ScholarCross RefCross Ref
  30. Stephen Gilroy, Julie Porteous, Fred Charles, and Marc Cavazza. 2012. Exploring passive user interaction for adaptive narratives. In Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces. 119--128. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Pia Tikka, Aleksander Väljamäe, Aline W. de Borst, Roberto Pugliese, Niklas Ravaja, Mauri Kaipainen, and Tapio Takala. 2012. Enactive cinema paves way for understanding complex real-time social interaction in neuroimaging experiments. Frontiers in Human Neuroscience 6, 298.Google ScholarGoogle ScholarCross RefCross Ref
  32. Stephen Gilroy, Julie Porteous, Fred Charles, Marc Cavazza, Eyal Soreq, Gal Raz, Limor Ikar, Ayelet Or-Borichov, Udi Ben-Arie, Ilana Klovatch, and Talma Hendler. 2013. A brain-computer interface to a plan-based narrative. In Proceedings of IJCAI. 1997--2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Alexis Kirke. 2013. Many Worlds: The movie that watches its audience. BBC News. Retrieved from http://www.bbc.co.uk/news/technology-21429437.Google ScholarGoogle Scholar
  34. Matthew Pike, Richard Ramchurn, Steve Benford, and Max L. Wilson. 2016. #Scanners: Exploring the control of adaptive films using brain-computer interaction. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 5385--5396. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Chris Berka, Daniel J. Levendowski, Michelle N. Lumicao, Alan Yau, Gene Davis, Vladimir T. Zivkovic, Richard E. Olmstead, Patrice D. Tremoulet, and Patrick L. Craven. 2007. EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviation, Space, and Environmental Medicine78, 1 (2007), B231--B244.Google ScholarGoogle Scholar
  36. Ernst Niedermeyer and F. H. Lopes Da Silva. 1993. Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 3rd ed. Lippincott, Williams 8 Wilkins, Philadelphia.Google ScholarGoogle Scholar
  37. Michal Teplan. 2002. Fundamentals of EEG measurement. Measurement Science Review 2, 2 (2002), 1--11.Google ScholarGoogle Scholar
  38. Edward Cutrell and Desney Tan. 2007. BCI for passive input in HCI. In Proceedings of CHI’07.Google ScholarGoogle Scholar
  39. Laurent George and Anatole Lecuyer. 2010. An overview of research on “passive” brain-computer interfaces for implicit human-computer interaction. In Proceedings of the International Conference on Applied Bionics and Biomechanics (ICABB 2010), Workshop “Brain-Computer Interfacing and Virtual Reality.”Google ScholarGoogle Scholar
  40. G. Garcia-Molina, T. Tsoneva, and A. Nijholt. 2013. Emotional brain--computer interfaces. International Journal of Autonomous and Adaptive Communications Systems 6, 1, 9--25. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Pierre W. Ferrez and José del R. Millán. 2005. You are wrong! Automatic detection of interaction errors from brain waves. In Proceedings of the 19th International Joint Conference on Artificial Intelligence. 83269. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Desney Tan. 2006. Brain-computer interfaces: Applying our minds to human-computer interaction. In Proceedings of the CHI Workshop: “What is the Next Generation of Human-Computer Interaction?”Google ScholarGoogle Scholar
  43. Alan T. Pope, Edward H. Bogart, and Debbie S. Bartolome. 1995. Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology 40, 1 (1995), 187--195.Google ScholarGoogle ScholarCross RefCross Ref
  44. Anton Nijholt, Danny Plass-Oude Bos, and Boris Reuderink. 2009. Turning shortcomings into challenges: Brain--computer interfaces for games. Entertainment Computing 1, 2 (2009), 85--94.Google ScholarGoogle ScholarCross RefCross Ref
  45. Johnny Chung Lee and Desney S. Tan. 2006. Using a low-cost electroencephalograph for task classification in HCI research. In Proceedings of the UIST’06. 81--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Marvin Andujar and Juan E. Gilbert. 2013. Let's learn!: Enhancing user's engagement levels through passive brain-computer interfaces. In CHI'13 Extended Abstracts on Human Factors in Computing Systems. 703--708. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Jin Huang, Chun Yu, Yuntao Wang, Yuhang Zhao, Siqi Liu, Chou Mo, Jie Liu, Lie Zhang, and Yuanchun Shi. 2014. FOCUS: Enhancing children's engagement in reading by using contextual BCI training sessions. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems. 1905--1908. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Yomna Abdelrahman, Mariam Hassib, Maria Guinea Marquez, Markus Funk, and Albrecht Schmidt. 2015. Implicit engagement detection for interactive museums using braincomputer interfaces. In Proceedings of the 17th International Conference on HumanComputer Interaction with Mobile Devices and Services Adjunct (MobileHCI’15). ACM, New York, 838--845. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Daniel Szafir and Bilge Mutlu. 2012. Pay attention!: Designing adaptive agents that monitor and improve user engagement. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 11--20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Nicolas Schulz. 2016. CryENGINE Manual. Retrieved from http://docs.cryengine.com/display/SDKDOC1/Home.Google ScholarGoogle Scholar
  51. Herbert H. Jasper. 1958. The ten twenty electrode system of the international federation. Electroencephalography and Clinical Neurophysiology 10 (1958), 371--375.Google ScholarGoogle Scholar
  52. Noppadon Jatupaiboon, Setha Pan-ngum, and Pasin Israsena. 2013. Real-time EEG-based happiness detection system. The Scientific World Journal 13 (2013).Google ScholarGoogle Scholar
  53. Shuo Yan, Gangyi Ding, Hongsong Li, Ningxiao Sun, Yufeng Wu, Zheng Guan, Longfei Zhang, and Tianyu Huang. 2016. Enhancing audience engagement in performing arts through an adaptive virtual environment with a brain-computer interface. In Proceedings of the 21st International Conference on Intelligent User Interfaces. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Maureen Heneghan Tripp. 2015. Stagecraft theatre. Encyclopaedia Britannica, https://global.britannica.com/art/stagecraft.Google ScholarGoogle Scholar
  55. Chuck B. Gloman and Rob Napoli. 2007. Scenic Design and Lighting Techniques: A Basic Guide for Theatre. Taylor 8 Francis.Google ScholarGoogle Scholar
  56. Wade M. Vagias. 2006. Likert-Type Scale Response Anchors. Clemson International Institute for Tourism 8 Research Development, Recreation and Tourism Management. Clemson University.Google ScholarGoogle Scholar

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            • Published in

              cover image ACM Transactions on Interactive Intelligent Systems
              ACM Transactions on Interactive Intelligent Systems  Volume 7, Issue 4
              Special Issue on IUI 2016 Highlights
              December 2017
              134 pages
              ISSN:2160-6455
              EISSN:2160-6463
              DOI:10.1145/3166060
              Issue’s Table of Contents

              Copyright © 2017 ACM

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              Publication History

              • Published: 4 December 2017
              • Revised: 1 May 2017
              • Accepted: 1 May 2017
              • Received: 1 July 2016
              Published in tiis Volume 7, Issue 4

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