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http://dx.doi.org/10.5573/ieek.2013.50.11.206

The Design of Feature Selection Classifier based on Physiological Signal for Emotion Detection  

Lee, JeeEun (Graduate School of Biomedical Engineering, Yonsei University)
Yoo, Sun K. (Department of Medical Engineering, Yonsei University College of Medicine)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.50, no.11, 2013 , pp. 206-216 More about this Journal
Abstract
The emotion plays a critical role in human's daily life including learning, action, decision and communication. In this paper, emotion discrimination classifier is designed to reduce system complexity through reduced selection of dominant features from biosignals. The photoplethysmography(PPG), skin temperature, skin conductance, fontal and parietal electroencephalography(EEG) signals were measured during 4 types of movie watching associated with the induction of neutral, sad, fear joy emotions. The genetic algorithm with support vector machine(SVM) based fitness function was designed to determine dominant features among 24 parameters extracted from measured biosignals. It shows maximum classification accuracy of 96.4%, which is 17% higher than that of SVM alone. The minimum error features selected are the mean and NN50 of heart rate variability from PPG signal, the mean of PPG induced pulse transit time, the mean of skin resistance, and ${\delta}$ and ${\beta}$ frequency band powers of parietal EEG. The combination of parietal EEG, PPG, and skin resistance is recommendable in high accuracy instrumentation, while the combinational use of PPG and skin conductance(79% accuracy) is affordable in simplified instrumentation.
Keywords
Emotion; Physiological signal; Support vector machine; Genetic algorithm;
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1 안형철, 최진영, "[특집]지능로봇의 감성행동 기술 동향," Journal of the IEEK, vol.32(1), pp.50-59, 2005.
2 Jonghwa K and Ande E, "Emotion Recognition Based on Physiological Changes in Music Listening," Pattern Anal. Mach. Intell., IEEE Transact, pp.2067-2083 2008.
3 Ekman P. and Friesen WV, "Universals and Cultural Differences in the Judgments of FAcial Expressions of Emotion," J pers Soc Psychol, pp.712-714, 1987.
4 Lang PJ, "The Emotion Probe: Studies of Motivation and Attention," Am Psychol, pp.372-385, 1995.
5 LIU Guang-Yuan and HAO Min, "Emotion Recognition of Physiological Signals Based on Adaptive Hierarchical Genetic Algorithm," 2009 World Congress on Computer Science and Information Engineering, pp.670-674, 2009.
6 Xiaowei Niu, Liwan Chen and Qiang Chan, "Research on Genetic Algorithm based on Emotion recognition using physiological signals," ICCP Proceedings, pp.614-618, 2011.
7 WooJin Chio, "A classification analysis of negative emotion based on PPG signal using Fuzzy-GA,", July, 2007.
8 John Aleen, "Photoplethysmography and its application in clinical physiological measurement," Physiological measurement, vol.28(3), pp.1-39, 2007.   DOI   ScienceOn
9 Pierre Rainville, Bechara A, Naqvi N and Damasio AR, "Basic emotions are associated with distince patterns of cardiorespiratory activity," International Journal of Psychophysiology, pp.5-18, 2006.
10 Foteini Agrafioti, "ECG Pattern Analysis for Emotion Detection", IEEE Transactions on Affective Computing, vol.3, 2012.
11 Malik M., "Measurement of heart rate variability," Heart Rate Variability. Armonk, pp.33-132, 1995.
12 Zhai, J. and Barreto, A., "Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables," The 28th Annual International Conference Engineering in Medicine and Biology Society, pp.1355-1358, 2006.
13 Guanghua Wu, Guangyuan Liu and Min Hao, "The analysis of emotion recognition from GSR based on PSO," 2010 International Symposium on Intelligence Information Processing and Trusted Computing, pp.360-363, 2010.
14 Murugappan Murugappan, Nagarajan Ramachandran and Yaacob Sazali, "Classification of human emotion from EEG using discrete wavelet transform," J. Biomedical Science and Engineering, vol.3, pp.390-396, April, 2010.   DOI
15 Edgar Osuna, Robert Freund, and Federico Girosi, "Support Vector Machines: Training and Applications," C.B.C,I. Paper No.144, March, 1997.
16 Chih-Chung Chang and Chih-Jen Lin, "Libsvm: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology (TIST), vol.2, pp.3-27, 2011.
17 David E. Goldberg, "Genetic Algorithm in Search, Optimization, and Machine Learning," Addison_Wesley Professional, 1 edition, 1989.
18 Melanie Dumas, "Emotional Expression Recognition using Support Vector Machines," In Proceedings of International Conference on Multimodal Interfaces, 2001.
19 권오상, "감성로봇 현황과 추세," Journal of the IEEK, vol.28(12), pp.18-25, 2001.
20 Mu Li and Bao-Liang Lu, "Emotion Classification Based on Gamma-band EEG", 31th Annual International Conference of the IEEE EMBS, pp.1323-1326, Minnesota, USA, Sep 2009.
21 Jerritta Selvaraj, Murugappan Murugappan, Khairunizam Wan and Sazali Yaacob, "Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst," BioMedical Engineering Online, 2013.