Transsituational individual-specific biopsychological classification of emotions

IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans

Volumn 43, Issue 4, 2013, Pages 988-995

  Walter, Steffen ^a   Kim, Jonghwa ^b   Hrabal, David ^a   Crawcour, Stephen Clive ^c   Kessler, Henrik ^d   Traue, Harald C ^a  

^a UNIVERSITY OF ULM   (Germany)

^b UNIVERSITY OF AUGSBURG   (Germany)

^c DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^d UNIVERSITY OF BONN   (Germany)

Author keywords

Biopsychological analysis; Biosignals; Emotion recognition; Feature selection; Transsituational emotion

Indexed keywords

BIOPSYCHOLOGICAL ANALYSIS; BIOSIGNALS; CLASSIFICATION ALGORITHM; CLASSIFICATION OF EMOTIONS; CLASSIFICATION RATES; CROSS VALIDATION; EMOTION RECOGNITION; TRANSSITUATIONAL EMOTION;

FEATURE EXTRACTION; SPEECH RECOGNITION;

EXPERIMENTS; FEATURE EXTRACTION;

EID: 84887097509     PISSN: 10834427     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCA.2012.2216869     Document Type: Article

References (47)

1. H. Traue and H. Kessler, Psychologische Emotionskonzepte[psychology emotion concepts], in Natur und Theorie der Emotion, A. Stephan and H. Walter, Eds. Paderborn, Germany: Mentis, 2003, pp. 20-33.
2. L. Schmidt-Atzert, Klassifikationen von emotionen[classification of emotion], in Experimentelle Emotionspsychologie, W. Janke, M. Schmidt-Daffy, and G. Debus, Eds. Lengerich, Germany: Pabst, 2008, pp. 179-192.
3. P. Ekman and W. Friesen, Facial Linear Networks and Systems. Palo Alto, CA: Consulting Psychologists Press, 1978.
4. J. Kim and E. André, Emotion recognition based on physiological changes in listening music, IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 12, pp. 2067-2083, Dec. 2008.
5. J. Kim, Bimodal emotion recognition using speech and physiological changes, in Robust Speech Recognition and Understanding, M. Grimm and K. Kroschel, Eds. Vienna, Austria: I-Tech Education Publ., 2007, pp. 265-280.
6. C. Frantzidis, C. Bratsas, C. Papadelis, E. Konstandinidis, C. Pappas, and P. Bamidis, Towards emotion aware computing: An integrated approach using multi-channel neuro-physiological recordings and affective visual stimuli, IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 3, pp. 589-597, May 2010.
7. T. Schuster, S. Gruss, H. Kessler, A. Scheck, H. Hoffmann, and H. Traue, EEG-Pattern classification while viewing emotional pictures, in Proc. 3rd Int. Conf. Pervasive Technol. Related Assistive Environ., 2010, pp. 12-17.
8. P. Lang, The emotion probe: Studies of motivation and attention, Am. Psychol., vol. 50, no. 5, pp. 372-385, May 1995.
9. W. Wundt, Lectures on Human and Animal Psychology, J. E. Creighton and E. B. Titchener, Eds. New York: Macmillan, 1896.
10. H. Schlossberg, Three dimensions of emotions, Psychol. Rev., vol. 61, no. 2, pp. 81-88, Mar. 1954.
11. A. Mehrabian, Valence-arousal-dominance: A general frame-work for describing and measuring individual differences in temperament, Curr. Psychol. Develop., Learn., Personality, vol. 14, no. 4, pp. 261-292, Dec. 1996.
12. J. Russel, A circumplex model of affect, J. Personality Social Psychol., vol. 39, no. 6, pp. 1161-1178, Dec. 1980.
13. B. Cuthbert, H. Schupp, M. Bradley, N. Bierbaumer, and P. Lang, Brain potentials in affective picture processing: Covariation with autonomic arousal and affective report, Biol. Psychol., vol. 52, no. 2, pp. 95-111, Mar. 2000.
14. H. Schupp, B. Cuthbert, M. Bradley, C. Hillman, A. Hamm, and P. Lang, Brain processes in emotional perception: Motivated attention, Cognit. Emotion, vol. 18, no. 5, pp. 563-611, Aug. 2004.
15. H. T. Schuppa, J. Stockburgera, F. Bublatzkya, M. Junghöferb, A. I. Weikec, and A. O. Hamm, The selective processing of emotional visual stimuli while detecting auditory targets: An ERP analysis, Brain Res., vol. 1230, pp. 168-176, Sep. 2008.
16. J. Helmert, F. Schrammel, S. Pannasch, and B. Velichkovsky, Human interaction with emotional virtual agents: Differential effects of agents' attributes on eye movement and EMG parameters, in Proc. Perception 36 ECVP Abstract Suppl., 2007, p. 28.
17. P. J. Lang, M. Greenwald, M. M. Bradley, and A. O. Hamm, Looking at pictures: Affective, facial, visceral and behavioral reactions, Psychophysiology, vol. 30, no. 3, pp. 261-273, May 1993.
18. D. Sabatinelli, M. Bradley, J. Fitzsimmons, and P. Lang, Parallel amygdala and inferotemporal activation reflect emotional intensity and fear relevance, NeuroImage, vol. 24, no. 4, pp. 1265-1270, Feb. 2005.
19. J. T. Cacioppo, Feeling and emotions: Roles for electrophysiological markers, Biol. Psychol., vol. 67, no. 1/2, pp. 235-243, Oct. 2004.
20. J. Coan and J. Allen, Frontal EEG asymmetry as a moderator and mediator of emotion, Biol. Psychol., vol. 67, no. 1/2, pp. 7-49, Oct. 2004.
21. E. Marosi, O. Bazán, G. Yañez, J. Bernal, T. Fernández, M. Rodrguez, J. Silva, and A. Reyes, Narrow-band spectral measurements of EEG during emotional tasks, Int. J. Neurosci., vol. 112, no. 7, pp. 871-891, Jul. 2002.
22. J. A. Healey, Wearable and automotive systems for affect recognition from physiology, Ph.D. dissertation, MIT, Cambridge, MA, 2000.
23. J. A. Healey and R. W. Picard, Detecting stress during realworld driving tasks using physiological sensors, IEEE Trans. Intell. Transp. Syst., vol. 6, no. 2, pp. 156-166, Jun. 2005.
24. J. Wang and Y. Gong, Recognition of multiple drivers' emotional state, in Proc. Int. Conf. Pattern Recognit., 2008, pp. 1-4.
25. C. L. Lisetti and F. Nasoz, Using noninvasive wearable computers to recognize human emotions from physiological signals, Eurasip J. Appl. Signal Process., vol. 2004, pp. 1672-1687, Jan. 2004.
26. G. Chanel, J. J. Kierkels, M. Soleymani, and T. Pun, Short-term emotion assessment in a recall paradigm, Int. J. Hum.-Comput. Stud., vol. 67, no. 8, pp. 607-627, Aug. 2009.
27. J. J. Kierkels, M. Soleymani, and T. Pun, Queries and tags in affectbased multimedia retrieval, in Proc. Int. Conf.Multimedia Expo-Special Session Implicit Tagging, 2009, pp. 1436-1439.
28. M. Soleymani, G. Chanel, L. J. M. Kierskels, and T. Pun, Affective characterization of movie scenes based on content analysis and physiological changes, Int. J. Semantic Comput., vol. 3, no. 2, pp. 235-254, Jun. 2009.
29. L. Li and J. H. Chen, Emotion recognition using physiological signals, in Lecture Notes in Computer Science. New York: Springer-Verlag, 2006.
30. P. Lang, M. Bradley, and B. Cuthbert, International Affective Picture System (IAPS): Affective ratings of pictures and instruction manual, Univ. Florida, Gainesville, FL, Tech. Rep., 2008.
31. R. Picard, E. Vyas, and J. Healy, Towards machine emotional intelligence, IEEE Trans. Pattern Anal.Mach. Intell., vol. 23, no. 10, pp. 1175-1191, Oct. 2001.
32. J. I. Lacey, D. Bateman, and R. Vanlehn, Autonomic response specificity: An experimental study, Psychosomatic Med., vol. 15, no. 1, pp. 8-21, Jan. 1953.
33. J. Fahrenberg and F. Foerster, Covariation and consistency of activation parameters, Biol. Psychol., vol. 15, no. 3/4, pp. 151-169, Nov./Dec. 1982.
34. P. Kuppens, I. V. Meuchelen, J. B. Nezlek, D. Dossche, and T. Timmermanns, Individual differences in core affect variability and their relationship to personality and psychological adjustment, Emotion, vol. 7, no. 2, pp. 262-274, May 2007.
35. J. D. Haynes and G. Rees, Decoding mental states form brain activity in humans, Nat. Rev. Neurosci., vol. 7, no. 7, pp. 523-534, Jul. 2006.
36. G. Stemmler and J. Wacker, Personality, emotion, and individual differences in physiological reponses, Biol. Psychol., vol. 84, no. 3, pp. 541-551, Jul. 2010.
37. P. Rani, N. Sakar, and J. Adams, Anxiety-based affective communication for implicit human-machine interaction, Adv. Eng. Inform., vol. 21, no. 3, pp. 323-334, Jul. 2007.
38. C. Liu, K. Conn, N. Sakar, andW. Stone, Physiology-based affect recognition for computer-assisted intervention of children with autism spectrum disorder, Int. J. Hum.-Comput. Stud., vol. 66, no. 9, pp. 662-677, Sep. 2008.
39. P. Rani, C. Liu, and N. Sakar, Interaction between human and robot-An affect-inspired approach, Interact. Stud., vol. 9, no. 2, pp. 230-257, 2008.
40. C. Liu, K. Conn, N. Sakar, and W. Stone, Online affect detection and robot behavior adaptation for intervention of children with autism, IEEE Trans. Robot., vol. 24, no. 4, pp. 883-896, Aug. 2008.
41. J. Cohen, A power primer, Psychol. Bull., vol. 112, no. 1, pp. 155-159, Jul. 1992.
42. N. O. Bernsen, H. Dybkjaer, and L. Dbkjaer, Wizard of Oz prototyping: How and when' presented at the Proc. CCI Working Papers Cognit. Sci./HCI, Roskilde, Denmark, 1994, Paper WPCS-94-1.
43. J. F. Kelley, An iterative design methodology for user-friendly natural language office information applications, ACM Trans. Inf. Syst., vol. 2, no. 1, pp. 26-41, Jan. 1984.
44. M. M. Bradley and P. J. Lang, Measuring emotion: The Self-Assessment Manikin and the semantic differential, J. Behav. Ther. Exp. Psychiat., vol. 25, no. 1, pp. 49-59, Mar. 1994.
45. R. Rangayyan, Biomedical Signal Analysis: A Case-Study Approach. Piscataway, NJ: IEEE Press, 2001, ser. Biomedical Engineering.
46. J. C. Principe, Artificial neural networks, in The Electrical Engineering Handbook, 2nd ed., R. C. Dorf, Ed. Boca Raton, FL: CRC Press, 1997.
47. M. Sherif and C. I. Hovland, Social Judgment: Assimilation and Contrast Effects in Communication and Attitude Change. Oxford, U.K.: Yale Univ. Press, 1961.