Skip to main content
SpringerLink
Log in

Emotion Recognition Based on Physiological Signals

  • Conference paper
Advances in Brain Inspired Cognitive Systems (BICS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7366))

Included in the following conference series:

Abstract

Emotion recognition has aroused great concern recently. Physiological signals show its objective in the field of emotion recognition. This paper introduces emotion recognition system and physiological signals processing. The recognition system can be divided into four sections: signal preprocessing, biological feature extraction, feature matching to and feature classification. For each part, we studied existed methods and discussed their performance and characteristics. Lastly, the trend of emotion recognition for physiological signals was given.

This work was supported in part by 985 Funding Project (3rd Phase) of Minzu University of China (Grant 9850100300107), Independent Research Funding Project of Minzu University of China ( Multisource information based Research on Ethnic Relationship) and Youth Funding Project of Minzu University of China (Anthropology based multimode Ethnic Facial Information Coding Research), Beijing Municipal Public Information Resources Monitoring Project (Grant 104-00102211)

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Minsky, M.: The Society of Mind. Simon & Schuster, New York (1985)

    Google Scholar 

  2. Hu, B., Tan, T., Wang, Y.: Affective computing - computer science and technology development of a new topic, 3rd edn., March 24. Science Times (2000) (in Chinese)

    Google Scholar 

  3. Picard, R.W.: Affective Computing. MIT Press, London (1997)

    Google Scholar 

  4. Mao, X., Xue, Y.: Human-computer emotional interaction. Science Press, Beijing (2011) (in Chinese)

    Google Scholar 

  5. Luo, S., Pan, L.: Affective Computing Theory and Technology. Systems Engineering and Electronics 25(7), 905–909 (2003) (in Chinese)

    Google Scholar 

  6. Palomba, D., Sarlo, M., Angrilli, A., et al.: Cardiac responses associated with affective processing of unpleasant film stimuli. International Journal of Psychophysiology 36(1), 45–57 (2000)

    Article  Google Scholar 

  7. Elias, V., Picard, R.: Affective pattern classification. MIT Media Laboratory, Florida (1998)

    Google Scholar 

  8. Picard, R., Jennifer, H.: Digital processing of affective signals. MIT Media Laboratory, Massachusetts (1998)

    Google Scholar 

  9. Picard, R., Vyzas, E., Healey, J.: Toward machine emotional intelligence analysis of affective physiological state. IEEE Transactions Pattern Analysis and Machine Intelligence 23(10), 1175–1191 (2001)

    Article  Google Scholar 

  10. Ahn, H., Pard, R.W.: Affective Cognitive Learning and Decision Making: The Role of Emotions. MIT Media Laboratory, Cambridge (2006)

    Google Scholar 

  11. Jiang, D.: Based on wavelet (packet) transform the ECG signal feature extraction method. Southwest University Journal (2009) (in Chinese)

    Google Scholar 

  12. Xu, J., Liang, N., Wang, Y., Wang, X.: ERP of the implicit self-esteem. Psychological Science 28(4), 792–796 (2005) (in Chinese)

    Google Scholar 

  13. Zhou, J.: Based on rough sets and SVM speech emotion recognition. Journal of Southwest Jiaotong University (2007) (in Chinese)

    Google Scholar 

  14. Zhang, W.: Semi-supervised SVM-based intrusion detection. Journal of Hebei University (2009) (in Chinese)

    Google Scholar 

  15. Wang, W.: Improved Fuzzy C-means clustering algorithm. Journal of Dalian Maritime University (2007) (in Chinese)

    Google Scholar 

  16. Jiang, J.: Based on the natural picture, face detection and recognition system. Journal of Dalian University of Technology (2008) (in Chinese)

    Google Scholar 

  17. Jung, T.P., Makeig, S.: Estimating Alertness from the EEG Power Spectrum. IEEE Trans. BME 44(1), 60–69 (1997)

    Article  Google Scholar 

  18. Oken, B.S., Kishiyama, S.S., Salinsky, M.C.: Pharmacologically Induced Changes in Arousal: Effects on Behavioral and Electrophysiological Measures of Alertness and Attention. Electrophysiology and clinical Neurophysiology 95(5), 359–371 (1995)

    Article  Google Scholar 

  19. Liang, S.F., Lin, C.T.: Monitoring Driver’s Alertness Based on the Driving Performance Estimation and the EEG Power Spectrum Analysis. In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology Society, pp. 5738–5741 (2005)

    Google Scholar 

  20. Vuckovic, A., Radivojevic, V.: Antomatic Recognition of Alertness and Drowsiness from EEG by an Artificial Neural Network. Medical Engineering and Physics 24(5), 349–360 (2002)

    Article  Google Scholar 

  21. Tononi, G., McIntosh, A.R.: Functional Clustering: Identifying Strongly Interactive Brain Regions in Neuroimaging Data. Neuroimage 7, 133–149 (1998)

    Article  Google Scholar 

  22. Lin, Y.: The speech signal-based emotion recognition research. South China University of Technology Doctoral Dissertation (2006) (in Chinese)

    Google Scholar 

  23. Isabelle, G., Andre, E.: An Introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)

    MATH  Google Scholar 

  24. Bian, Z., Gong, Z.: Pattern Recognition, 2nd edn. Tsinghua University Press, Beijing (2000) (in Chinese)

    Google Scholar 

  25. Yang, R.: Emotional recognition of the physiological signal based on BPSO. Southwest University (2008) (in Chinese)

    Google Scholar 

  26. Micheloyannis, S., Sakkalis, V., Vourkas, M., Stam, C.J., Simos, P.G.: Neural networks involved in mathematical thinking: Linear and non-linear analysis of electroencephalographic activity. Neuroscience Letters 373, 212–217 (2005)

    Article  Google Scholar 

  27. Wang, S.: A hidden Markov model-based speech emotion recognition. Science and Technology Information (28) (2010) (in Chinese)

    Google Scholar 

  28. Nyklicek, I., Thayer, J.F., Van Doornen, L.J.P.: Cardio respiratory differentisyion of musically-induced emotions. Journal of Psychophysiology 11(4), 304–321 (1997)

    Google Scholar 

  29. Li, J., Zhang, P., Wang, L., Dai, J., Yan, K.: Experimental study of the five kinds of basic emotions, autonomic nervous system response patterns specificity. Behavior of Chinese Medical Sciences 14(3), 257–259 (2005) (in Chinese)

    Google Scholar 

  30. Rainville, P., Bechara, A., Naqvi, N., et al.: Basic emotions are associated with distinct patterns of cardio respiratory activity. International Journal of Psychophysiology 61(1), 5–18 (2006)

    Article  Google Scholar 

  31. Fu, J.: EEG-based preference analysis. Journal of Shanghai Jiaotong University (2009) (in Chinese)

    Google Scholar 

  32. Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming, New York, pp. 591–600 (1998)

    Google Scholar 

  33. Cai, L., Wang, Z.: EMG identification method based on wavelet transforms. Data acquisition and processing. Department of Biomedical Engineering of Shanghai Jiaotong University (June 2000) (in Chinese)

    Google Scholar 

  34. Liu, N., Lei, M.: Characteristics of surface EMG action based on wavelet and nonlinear indicators. Tissue engineering research and clinical rehabilitation. State Key Laboratory of Shanghai Jiaotong University Mechanical System and Vibration (2008) (in Chinese)

    Google Scholar 

  35. Shao, X., Rui, K.: Different intensity stimulation internally, the extraversion heart rate, the preliminary study of the impact of the T-wave amplitude. Yin. Psychological Science 27(2), 466–467 (2004) (in Chinese)

    Google Scholar 

  36. Wang, X., Li, X., Xiong, G.: Comparative analysis of short-range heart rate variability and long-term heart rate variability. Journal of Clinical Electrocardiology 11(1), 11–13 (2002) (in Chinese)

    Google Scholar 

  37. Wen, W., Liu, G.: Physiological signal-based emotion recognition method research, pp. I-II. Southwestern University, Chongqing (2010) (in Chinese)

    Google Scholar 

  38. Wu, X., Ren, L., Ding, Y., Wu, Y.: Emotional discrimination of multiple physiological information fusion for smart clothing. Computer Engineering and Applications 33, 218–221 (2009) (in Chinese)

    Google Scholar 

  39. Chen, J., Li, L., Qian, K.: Preliminary identification based on multiple physiological signals of sentiment. Biomedical Engineering Research (25), 141–146 (2006) (in Chinese)

    Google Scholar 

  40. Wagner, J., Kim, J., Andre, E.: From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification. In: IEEE International Conference on Multimedia & Expo (ICME 2005), pp. 940–943 (2005)

    Google Scholar 

  41. Litman, D., Forbes, K.: Recognizing Emotions from Student Speech in Tutoring Dialogues. In: IEEE Workshop on Automatie on Automate Speech Recognition and Understanding, pp. 25–30 (2003)

    Google Scholar 

  42. Wagner, J., Kim, J., Andre, E.: From Physiological Signals to Emotions: Implemeniing and Comparing Selected Methods for Feature Extraction and Classification (2005)

    Google Scholar 

  43. Kim, K.H., Bang, S.W., Kim, S.R.: Emotion recognition system using short-term monitoring of physiological signals. Med. Biol. Eng. Compute. 42, 419–427 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Engineering College, Minzu University of China, Beijing, 100081, China

    Naiyu Wu, Huiping Jiang & Guosheng Yang

  2. College of Science, Minzu University of China, Beijing, 100081, China

    Naiyu Wu

Authors
  1. Naiyu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huiping Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guosheng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, 110004, Shenyang, P.R. China

    Huaguang Zhang

  2. Dept. of Computing Science and Mathematics, University of Stirling, FK9 4LA, Stirling, Scotland, UK

    Amir Hussain

  3. Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China

    Derong Liu

  4. Northeastern University, School of Information Science and Engineering, 110004, Shenyang, Liaoning, People’s Republic of China

    Zhanshan Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wu, N., Jiang, H., Yang, G. (2012). Emotion Recognition Based on Physiological Signals. In: Zhang, H., Hussain, A., Liu, D., Wang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2012. Lecture Notes in Computer Science(), vol 7366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31561-9_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31561-9_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31560-2

  • Online ISBN: 978-3-642-31561-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation