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Construction and cross-correlation analysis of the affective physiological response database

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Abstract

Six kinds of emotions, namely joy, surprise, disgust, grief, anger and fear, are elicited from 300 ordinary college students by means of high-rated movie clips. The signals of galvanic skin response (GSR), heart rate (HR), blood volume pulse (BVP), electrocardiogram (ECG), respiration (Rsp.), facial electromyography (EMG) and two channels of electroencephalography (EEG) from the frontal lobe were recorded while the subjects were watching the affective eliciting materials. The samples of each kind of signals are analyzed by using random matrix theory (RMT). From the distribution of the eigenvalues and eigenvectors of the correlation matrices of BVP, facial EMG and two EEGs, we find that the statistical properties of these four kinds of signals coincide with the RMT predictions. However, the largest eigenvalue and the corresponding eigenvector component distribution of the correlation matrices of GSR, HR, ECG and Rsp. have deviated significantly from the RMT predictions. Our experiments also reveal that the correlations between two arbitrary samples of BVP, facial EMG or EEGs does not result from affective response but from random noises, whereas the amplitude variations of GSR, HR, ECG and Rsp. contain the correlated affective physiological response patterns. According to the data analysis results of RMT, 110 features are firstly extracted from the GSR and HR signals, and then a subset of the initial features is selected by using the Backward Selection algorithm. The affective data samples each of which is a vector of the features are classified by using a Fisher classifier, and furthermore, the user-independent emotion recognition systems with good prediction performance are constructed during the model selection process.

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References

  1. Li J P, Zhang P, Wang L F, et al. Experiment study of autonomic nervous response patterns in five basic emotions (in Chinese). Chin J Behav Med Sci, 2005, 14: 257–259

    Google Scholar 

  2. Christie I C, Friedman B H. Autonomic specificity of discrete emotion and dimensions of affective space: a multivariate approach. Int J Psychophys, 2004, 51: 143–153

    Article  Google Scholar 

  3. Bradley M M, Lang P J. Affective reactions to acoustic stimuli. Psychophysiology, 2000, 37: 204–215

    Article  Google Scholar 

  4. Cacioppo J T, Berntson G G, Larsen J T, et al. The psychophysiology of emotion. In: Lewis M, Haviland-Jones J M, eds. The Handbook of Emotion, New York: Guilford Press, 2000. 173–191

    Google Scholar 

  5. Gomez P, Stahel W A, Danuser B. Respiratory responses during affective picture viewing. Biolog Psych, 2004, 67: 359–373

    Article  Google Scholar 

  6. Gomez P, Zimmermann P, Guttormsen-Schär S, et al. Respiratory responses associated with affective processing of film stimuli. Biolog Psych, 2005, 68: 223–235

    Article  Google Scholar 

  7. Nyklicek I, Thayer J F, Van Doornen L J P. Cardiorespiratory differentiation of musically-induced emotions. J Psychophys, 1997, 11: 304–321

    Google Scholar 

  8. Palomba D, Sarlo M, Angrilli A, et al. Cardiac responses associated with affective processing of unpleasant film stimuli. Int J Psychophys, 2000, 36: 45–57

    Article  Google Scholar 

  9. Rainville P, Bechara A, Naqvi N, et al. Basic emotions are associated with distinct patterns of cardiorespiratory activity. Int J Psychophys, 2006, 61: 5–18

    Article  Google Scholar 

  10. Picard R W, Vyzas E, Healey J. Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Trans Patt Anal Mach Intell, 2001, 23: 1175–1191

    Article  Google Scholar 

  11. Haag A, Goronzy S, Schaich P, et al. Emotion recognition using bio-sensors: First steps towards an automatic system. In: Proceedings of the Kloster Irsee Tutorial and Research workshop on Affective Dialogue Systems. Berlin: Springer-Verlag, 2004. 36–48

    Chapter  Google Scholar 

  12. Wagner J, Kim J, André E. From physiological signals to emotions: Implementing and comparing selected methods for feature extraction and classification. In: Proceedings of IEEE International Conference on Multimedia & Expo. Los Alamitos: IEEE Computer Society Press, 2005. 940–943

    Chapter  Google Scholar 

  13. Kim K H, Bang S W, Kim S R. Emotion recognition system using short-term monitoring of physiological signals. Med Biolog Eng Comput, 2004, 42: 419–427

    Article  Google Scholar 

  14. Kim J, André E. Emotion recognition based on physiological changes in music listening. IEEE Trans Patt Anal Mach Intell, 2008, 30: 2067–2083

    Article  Google Scholar 

  15. Li L, Chen J H. Emotion recognition using physiological signals. In: Pan Z G, Cheok A, Haller M, et al. eds. Lecture Notes in Computer Science. Berlin: Springer, 2006. 437–446

    Google Scholar 

  16. Nasoz F, Lisetti C L, Alvarez K, et al. Emotion recognition from physiological signals for presence technologies. Int J Cognit Tech Work, 2003, 6: 4–14

    Article  Google Scholar 

  17. Plerou V, Gopikrishnan P, Rosenow B, et al. Universal and nonuniversal properties of cross correlations in financial time series. Phys Rev Lett, 1999, 83: 1471–1474

    Article  Google Scholar 

  18. Barthelemy M, Gondran B, Guichard E. Large scale cross-correlations in Internet traffic. Phys Rev E, 2002, 66: 056110.1–056110.7.

    Article  Google Scholar 

  19. Mehta M L. Random Matrices. Boston: Academic Press, 2004

    MATH  Google Scholar 

  20. Cheng N P, Zheng R R, Zhu S Q. Quasiparticle features and level statistics of odd-odd nucleus (in Chinese), Acta Phys Sin, 2001, 50: 1035–1039

    Google Scholar 

  21. Kapoor A, Burleson W, Picard R W. Automatic prediction of frustration. Int J Human-Comput Stud, 2007, 65: 724–736

    Article  Google Scholar 

  22. Isabelle G, Andre E. An introduction to variable and feature selection. J Mach Learn Res, 2003, 3: 1157–1182

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    WanHui Wen & GuangYuan Liu

  2. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    YuHui Qiu

  3. School of Materials Science and Engineering, Southwest University, Chongqing, 400715, China

    NanPu Cheng

  4. School of Psychology, Southwest University, Chongqing, 400715, China

    XiTing Huang

Authors
  1. WanHui Wen
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  2. YuHui Qiu
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  3. GuangYuan Liu
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  4. NanPu Cheng
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  5. XiTing Huang
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Correspondence to GuangYuan Liu.

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Wen, W., Qiu, Y., Liu, G. et al. Construction and cross-correlation analysis of the affective physiological response database. Sci. China Inf. Sci. 53, 1774–1784 (2010). https://doi.org/10.1007/s11432-010-4001-1

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  • DOI: https://doi.org/10.1007/s11432-010-4001-1

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