Predictor-based Control of Excitement as Human Response Signal to a Dynamic Virtual 3D Face

This paper introduces the application of predictor-based control with constraints of human response to a dynamic virtual 3D face. We are using changing distance-between-eyes in a woman 3D face as a stimulus - control signal. Human response to the stimulus is observed using EEG-based excitement signal - output signal. The technique of dynamic systems identification which ensures stability and possible higher gain of the model for building a predictive input-output model of control plant is applied. Two predictor-based control schemes with a minimum variance or a generalized minimum variance control quality and constrained control signal magnitude and change rate are developed. High prediction accuracy and control quality are demonstrated by modelling results.

[1]  Ronald Soeterboek,et al.  Predictive Control: A Unified Approach , 1992 .

[2]  Ausra Vidugiriene,et al.  Predictor-based control of human emotions when reacting to a dynamic virtual 3D face stimulus , 2015, 2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO).

[3]  Karl Johan Åström,et al.  Computer-Controlled Systems: Theory and Design , 1984 .

[4]  Vytautas Kaminskas,et al.  Practical issues in the implementation of predictor-based self-tuning control systems , 1993 .

[5]  E. Camacho,et al.  Generalized Predictive Control , 2007 .

[6]  E. Lamounier,et al.  On the Agile Development of Virtual Reality Systems , 2015 .

[7]  Yuan-Pin Lin,et al.  EEG-Based Emotion Recognition in Music Listening , 2010, IEEE Transactions on Biomedical Engineering.

[8]  David Clarke,et al.  Advances in model-based predictive control , 1994 .

[9]  Vytautas Kaminskas,et al.  Self-Tuning Constrained Control of a Power Plant , 1989 .

[10]  Vytautas Kaminskas,et al.  Minimum variance control of human emotion as reactions to a dynamic virtual 3D face , 2016, 2016 IEEE 4th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE).

[11]  Vytautas Kaminskas,et al.  Self-Tuning Control of a Stochastic Non-Linear Object , 1990 .

[12]  George Caridakis,et al.  Affective, Natural Interaction Using EEG: Sensors, Application and Future Directions , 2012, SETN.

[13]  V. Peterka,et al.  Predictor-based self-tuning control , 1982, Autom..

[14]  Jordan J. Louviere,et al.  Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking , 2013, Expert Syst. Appl..

[15]  K. Åström Introduction to Stochastic Control Theory , 1970 .

[16]  Suprijanto,et al.  Development System for Emotion Detection Based on Brain Signals and Facial Images , 2009 .

[17]  Ausra Vidugiriene,et al.  A Comparison of Hammerstein-Type Nonlinear Models for Identification of Human Response to Virtual 3D Face Stimuli , 2016, Informatica.

[18]  CONSTRAINED SELF-TUNING CONTROL OF STOCHASTIC EXTREMAL SYSTEMS , 1991 .

[19]  Olga Sourina,et al.  A Fractal-based Algorithm of Emotion Recognition from EEG using Arousal-Valence Model , 2011, BIOSIGNALS.

[20]  Tegan Harrison,et al.  The Emotiv mind: Investigating the accuracy of the Emotiv EPOC in identifying emotions and its use i , 2013 .

[21]  Venkataramanan Balakrishnan,et al.  System identification: theory for the user (second edition): Lennart Ljung; Prentice-Hall, Englewood Cliffs, NJ, 1999, ISBN 0-13-656695-2 , 2002, Autom..

[22]  Vytautas Kaminskas,et al.  Predictor-Based Control of Human Response to a Dynamic 3D Face Using Virtual Reality , 2018, Informatica.

[23]  Vytautas Kaminskas,et al.  Predictor-Based Self Tuning Control with Constraints , 2007 .

[24]  Vytautas Kaminskas,et al.  Self-Tuning Control of the Nuclear Reactor Power , 1990 .

[25]  Rolf Isermann Digital Control Systems , 1981 .

[26]  Nadine Gottschalk,et al.  Computer Controlled Systems Theory And Design , 2016 .

[27]  Ausra Vidugiriene,et al.  Modeling Human Emotions as Reactions to a Dynamical Virtual 3D Face , 2014, Informatica.

[28]  Rana El Kaliouby,et al.  Emotion detection using noisy EEG data , 2010, AH.

[29]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[30]  Ausra Vidugiriene,et al.  Identification of Human Response to Virtual 3D Face Stimuli , 2014, Inf. Technol. Control..