Enhancing Audience Engagement in Performing Arts Through an Adaptive Virtual Environment with a Brain-Computer Interface

Audience engagement is an important indicator of the quality of the performing arts but hard to measure. Psychophysiological measurements are promising research methods for perceiving and understanding audience's responses in real-time. Currently, such research are conducted by collecting biometric data from audience when they are watching a performance. In this paper, we draw on techniques from brain-computer interfaces (BCI) and knowledge from quality of performing arts to develop a system that monitor audience engagement in real time using electroencephalography (EEG) measurement and seek to improve it by triggering the adaptive performing cues when the engagement level decreased. We simulated the immersive theatre performances to provide audience a high-fidelity visual-audio experience. An experimental evaluation is conducted with 48 participants during two different performance studies. The results showed that our system could successfully detect the decreases in audience engagement and the performing cues had positive effects on regain audience engagement. Our research offers the guidelines for designing theatre performances from the audience's perception.

[1]  Hasan Ayaz,et al.  Assessment of Cognitive Neural Correlates for a Functional Near Infrared-Based Brain Computer Interface System , 2009, HCI.

[2]  Chen Wang,et al.  Sensing a live audience , 2014, CHI.

[3]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[4]  Anton Nijholt,et al.  Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games , 2009, INTETAIN.

[5]  Pasin Israsena,et al.  Real-Time EEG-Based Happiness Detection System , 2013, TheScientificWorldJournal.

[6]  Steve Dixon,et al.  Digital Performance: A History of New Media in Theater, Dance, Performance Art, and Installation , 2007 .

[7]  A. Walker Electroencephalography, Basic Principles, Clinical Applications and Related Fields , 1982 .

[8]  Celine Latulipe,et al.  Love, hate, arousal and engagement: exploring audience responses to performing arts , 2011, CHI.

[9]  Desney S. Tan,et al.  Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction , 2010 .

[10]  P. Lang The emotion probe. Studies of motivation and attention. , 1995, The American psychologist.

[11]  Robert J. K. Jacob,et al.  Designing a passive brain computer interface using real time classification of functional near-infrared spectroscopy , 2013, Int. J. Auton. Adapt. Commun. Syst..

[12]  Hilary Glow,et al.  Hidden stories : listening to the audience at the live performance , 2010 .

[13]  Michelle N. Lumicao,et al.  EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. , 2007, Aviation, space, and environmental medicine.

[14]  Christian Kothe,et al.  Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general , 2011, Journal of neural engineering.

[15]  Rob Napoli,et al.  Scenic Design and Lighting Techniques: A Basic Guide for Theatre , 2006 .

[16]  José del R. Millán,et al.  You Are Wrong! - Automatic Detection of Interaction Errors from Brain Waves , 2005, IJCAI.

[17]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[18]  G Pfurtscheller,et al.  Current trends in Graz Brain-Computer Interface (BCI) research. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[19]  J. Craig Henry,et al.  Creating Coordination in the Cerebellum: Progress in Brain Research, Volume 148 , 2006, Neurology.

[20]  Edward Cutrell,et al.  BCI for passive input in HCI , 2007 .

[21]  Juan E. Gilbert,et al.  Let's learn!: enhancing user's engagement levels through passive brain-computer interfaces , 2013, CHI Extended Abstracts.

[22]  Jie Liu,et al.  FOCUS: enhancing children's engagement in reading by using contextual BCI training sessions , 2014, CHI.

[23]  M. Teplan FUNDAMENTALS OF EEG MEASUREMENT , 2002 .

[24]  E. DeYoe,et al.  A physiological correlate of the 'spotlight' of visual attention , 1999, Nature Neuroscience.

[25]  Desney S. Tan,et al.  Using a low-cost electroencephalograph for task classification in HCI research , 2006, UIST.

[26]  Anton Nijholt,et al.  Emotional brain-computer interfaces , 2009, ACII.

[27]  Timothy A. Pedley,et al.  Book reviewElectroencephalography. Basic principles, clinical applications, and related fields, 3rd edition: E. Niedermeyer and F. Lopes da Silva (Eds.) (Williams and Wilkins, Baltimore, MD, 1993, 1164 p., Price US $150.00) , 1994 .

[28]  Bilge Mutlu,et al.  Pay attention!: designing adaptive agents that monitor and improve user engagement , 2012, CHI.

[29]  Anatole Lécuyer,et al.  An overview of research on "passive" brain-computer interfaces for implicit human-computer interaction , 2010 .

[30]  A. Pope,et al.  Biocybernetic system evaluates indices of operator engagement in automated task , 1995, Biological Psychology.

[31]  D. E. Glaser,et al.  Towards a sensorimotor aesthetics of performing art , 2008, Consciousness and Cognition.

[32]  Gabriele Troilo,et al.  The drivers of hedonic consumption experience: a semiotic analysis of rock concerts , 2007 .

[33]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[34]  Jennifer Radbourne,et al.  Audience experience : measuring quality in the performing arts , 2009 .