Emotion Judgment System by EEG Based on Concept Base of EEG Features

doi:10.3969/j.issn.1673-5188.2017.S2.002

ZTE Communications ›› 2017, Vol. 15 ›› Issue (S2): 11-17.DOI: 10.3969/j.issn.1673-5188.2017.S2.002

收稿日期:2017-08-09 出版日期:2017-12-25 发布日期:2020-04-16

Emotion Judgment System by EEG Based on Concept Base of EEG Features

Mayo Morimoto¹, Misako Imono², Seiji Tsuchiya³, Hirokazu Watabe³

1. Graduate School of Science and Engineering, Doshisha University, Kyo-Tanabe City 6100394, Japan
2. Daido University, Nagoya City 4578530, Japan
3. Doshisha University, Kyo?Tanabe City 6100394, Japan

Received:2017-08-09 Online:2017-12-25 Published:2020-04-16
About author:Mayo Morimoto (eup1104@mail4.doshisha.ac.jp) received the Bachelor of Engineering from Faculty of Science and Engineering, Doshisha University, Japan in 2012 and the Master of Engineering from Graduate School of Science and Engineering, Doshisha University in 2014. She joined NEC Corporation in 2014. She has been studying for a doctorial degree in information engineering at the Graduate School of Science and Engineering, Doshisha University from 2015. Her research mainly focuses in EEG analysis.|Misako Imono (m-imono@daido-it.ac.jp) received the Bachelor of Engineering from Faculty of Engineering, Doshisha University, Japan in 2009. She received the Master of Engineering from Graduate School of Science and Engineering, Doshisha University in 2011. She received Ph.D. from Graduate School of Science and Engineering, Doshisha University in 2014. She was a research associate in Faculty of Science and Engineering, Doshisha University in 2015. She is a senior lecturer in Faculty of Information System, Daido University, Japan from 2016. Her research mainly focuses in concept processing.|Seiji Tsuchiya (stsuchiy@mail.doshisha.ac.jp) received the Bachelor of Engineering from Faculty of Engineering, Doshisha University, Japan in 2000. He received the Master of Engineering from Graduate School of Engineering, Doshisha University in 2002. He entered Sanyo Electric Co., Ltd in 2002. He received his Ph.D. from Graduate School of Engineering, Doshisha University in 2007. He was an assistant professor in Bulletin of Institute of Technology and Science the University of Tokushima, Japan. He was an assistant professor in Faculty of Science and Engineering, Doshisha University in 2009, an associate professor in 2011, and a professor in 2017 there. His research interests include knowledge processing, concept processing, and semantic interpretation.|Hirokazu Watabe (hwatabe@mail.doshisha.ac.jp) received the Bachelor of Engineering from Faculty of Engineering in Hokkaido University, Japan in 1983. He received the Master of Engineering from Graduate School of Engineering, Hokkaido University in 1985. He dropout Ph.D. course of Graduate School of Engineering, Hokkaido University in 1987. He was an research associate in Kyoto University, Japan in 1987. He was a senior lecturer in Faculty of Engineering, Doshisha University, Japan in 1994, an assistant professor in 1998, and a professor in 2006 there. His research interests include evolutionary computation method, computer vision, concept processing.

摘要/Abstract

Abstract:

This paper proposes an emotion judgment system by using an electroencephalogram (EEG) feature concept base with premise of noises included. This method references the word concept association system, which associates one word with other plural words and decides the relationship between several words. In this proposed emotion judgment system, the source EEG is input and 42 EEG features are constructed by EEG data; the data are then calculated by spectrum analysis and normalization. All 2945 EEG data of 4 emotions in the EEG data emotion knowledge base are calculated by the degree of association for getting the nearest EEG data from the EEG feature concept base constructed by 2844 concepts. From the experiment, the accuracy of the proposed system was 55.9%, which was higher than the support vector machine (SVM) method. As this result, the chain structured feature of the EEG feature concept base and the efficiency by the calculation of degree of association for EEG data help reduce the influence of the noise.

Key words: electroencephalogram, EEG, emotion judgment, concept base, calculation of degree of association

. [J]. ZTE Communications, 2017, 15(S2): 11-17.

Mayo Morimoto, Misako Imono, Seiji Tsuchiya, Hirokazu Watabe. Emotion Judgment System by EEG Based on Concept Base of EEG Features[J]. ZTE Communications, 2017, 15(S2): 11-17.

图/表 5

Figure 1. Emotion Judgment System by EEG.

Figure 2. Spectrum analysis of the source EEGs.

Figure 3. EEG measurement at 14 locations, conforming to the International 10/20 System.

Figure 4. Process for conceptualization of EEG features.

Figure 5. Comparison of accuracy of the emotion judgment methods.

参考文献 33

[1]	K. Takahashi , “Remarks on emotion recognition from multi-modal bio potential signals,” Japan Ergonomic Society, vol. 41, no. 4, pp. 248-253, Aug. 2005. doi: 10.5100/jje.41.248.
[2]	T. Satou and K. Nakagawa , “Quantification of emotion using fractal dimension analysis, emotion fractal-dimension analysis method,” IEICE Technical Report, vol. 102, no. 534, pp. 12-18, Dec. 2002.
[3]	Y. Ikeda, Y. Okada, R. Horie, M. Sugyaya , “Estimate Emotion Method to Use Facial Expressions, and Biological Information,” DICOMO2016, vol. 2016, pp. 149-161, Jul. 2016.
[4]	S. Tsuchiya, K. Kojima, T. Kawaoka , “Unknown word processing method for the common sense judgment system,” JSAI Technical Report, vol. 15, no. 6, pp. 667-675, Aug. 2002. doi: 10.1527/tjsai.17.667.
[5]	T. Hirose, H. Watabe, T. Kawaoka , “Automatic method of concept-base considering the rule between concepts and frequency of appearance as an attribute,” IEICE Technical Report, vol. 101, no. 712, pp. 109-116, Mar. 2002.
[6]	H. Watabe and T. Kawaoka , “Measuring degree of association between concepts for commonsense judges,” Natural Language Processing, vol. 8, no. 2, pp. 39-54, Mar. 2011. doi: 10.5715/jnlp.8.2_39.
[7]	N. Okumura, S. Tsuchiya, H. Watabe, T. Kawaoka , “A construction of large-scale of concept-base for calculation of degree of association between concepts,” Natural Language Processing, vol. 14, no. 5, pp. 41-64, Mar. 2011. doi: 10.5715/jnlp.14.5_41.
[8]	C. Chang and C. Lin , “LIBSVM: a library for support vector machine,” ACM Transaction on Intelligent System and Technology, vol. 2, no. 3, article no. 27, Apr. 2011. doi: 10.1145/1961189.1961199.
[9]	T. Hayashi, Y. Mizuno Matsumoto, E. Okamoto , et al., “Time course changes of brain function related to emotional stress,” The IEICE Transactions on Information and Systems (Japanese Edition), vol. 91, no. 7, pp. 1874-1885, Jul. 2008.
[10]	Y. Kono and N. Muranaka , “Estimation of brain activity area on sensitivity evaluation by measuring EEG,” IEICE Technical Report, NC, vol. 110, no. 335, pp. 69-72, Dec. 2010.
[11]	Y. Lin, C. Wang, T. Jung , et al., “EEG-based emotion recognition in music listening,” IEEE Transaction on Biomedical Engineering, vol. 57, no. 7, pp. 1798-1806, Jul. 2010. doi: 10.1109/TBME.2010.2048568.
[12]	A. Iwahara and T. Hatta , “Communication mechanism of the emotional-semantic information in written Japanese language,” Cognitive Studies, vol. 11, no. 3, pp. 271-281, Apr. 2007. doi: 10.11225/jcss.11.271.
[13]	Y. Liu, M. Yu, G. Zhao , et al., “Real-time movie-induced discrete emotion recognition from EEG signals,” IEEE Transaction on Affective Computing, no. 99, Jan. 2017. doi: 10.1109/TAFFC.2017.2660485.
[14]	K. Matsumoto, K. Mishina, R. Fuji, S. Kuroiwa , “Emotion estimation algorithm based on emotion occurrence sentence pattern,” Natural Language Processing, vol. 14, no. 3, pp. 239-271, Mar. 2011. doi: 10.5714/jnlp.14.3_239.
[15]	K. Mera, T. Ichimura, T. Aizawa, T. Yamashita , “Invoking emotions in a dialog system based on world-impressions,” JSAI Technical Report, vol. 7, no. 3, pp. 186-195, Apr. 2002. doi: 10.1527/tjsai.17.186.
[16]	J. Shigeno , “Integration of audiovisual information and cultural differences in the recognition of emotion,” IEICE Technical Report, vol. 105, no. 291, pp. 25-30, Sep. 2005.
[17]	M. Tokuhisa and N. Okada , “A pattern comprehension approach to emotion arousal of an intelligent agent,” Transaction of Information Processing Society of Japan, vol. 39, no. 8, pp. 2440-2451, Aug. 1998.
[18]	S. Tsuchiya, E. Yoshimura, H. Watabe, T. Kawaoka , “The method of the emotion judgment based on an association mechanism,” Natural Language Processing, vol. 14, no. 3, pp. 219-238, Mar. 2011. doi: 10.5715/jnlp.14.3_219.
[19]	H. Oohira , “ Chapter 1 kanjo no riron,” Kanjo Shinrigaku—Nyuumon. Tokyo, Japan: Yuhikaku, 2010.
[20]	A. Ortony, G. L. Clore, A. Collins , The Cognitive Structure of Emotions. Cambridge, England: Cambridge University Press, 1990.
[21]	S. Tsuchiya, M. Suzuki, H. Watabe , “Analytics of emotion notification tendency by the difference in media,” The IEICE Transactions, vol. J98-A, no. 1, pp. 103-112, Jan. 2015.
[22]	T. Musha, Y. Terasaki, H. A. Haque, G. A. Ivanitsk , “Feature extraction from EEGs associated with emotions,” Artificial Life and Robotics, vol. 1, no. 1, pp. 15-19, Mar. 1997.
[23]	Y. Araga and R. Musha , “Numeric quantification of human emotions with EEGs,” Bioscience&Industry, vol. 59, no. 10, pp. 693-694, Oct. 2001.
[24]	T. Shinmura, J. Akita, S. Sakurazawa, M. Toda , “Presice multi-channel electromyography measurement system using shield and power supply function of conductive wear,” IPSJ Journal, vol. 48, no. 12, pp. 3784-3792, Dec. 2007.
[25]	H. Kato and N. Toda , “A method for elimination of alternative noise by digital control of preamplifier in biopotential measurements,” The IEICE Transactions, vol. J87-D2, no. 12, pp. 2253-2261, 2004.
[26]	H. Jasper , “The ten twenty electrode system of the international federation,” Electroencephalography and Clinical Neurophysiology, vol.10, pp. 371-375, 1959.
[27]	Brain Functions Laboratory. (2017). Sensibility spectrum analysis system ESA PRO series [Online]. Available: http://www.bfl.co.jp/products
[28]	H. Hotta, K. Nakayama, A. Hirano , “Neural network based BCI by using orthogonal components of multi-channel,” in 23rd Digital Signal Processing Symposium, Kanazawa, Japan, 2008, pp. 176-181.
[29]	K. Tokunaga and J. Tsujii , Jyouhoukennsaku-to-Gengoshori. Tokyo, Japan: University of Tokyo Press, 1999.
[30]	Y. Seki and K. Harada , “Dynamic document generation based on tf/idf weighting,” IPSJ SIG Technical Report, vol. 2001, no. 120, pp. 25-32, Nov. 2001.
[31]	L. J. Prinzel, F. G. Freeman, M, W. Scerbo, P. J. Mikulka, and A. T. Pope , “A close-loop system for examining psychophysiological measure for adaptive task allocation,” The International Journal of Aviation Psychology, vol. 10, no. 4, pp. 393-410, Nov. 2009. doi: 10.1207/S15327108IJAP1004_6.
[32]	R. J. Genili, J. C. Rietschel, K. J. Jaquess , et al., “Brain biomarkers based assessment of cognitive workload in pilot under various task demands,” in 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, USA, 2014, pp. 5860-5863. doi: 10.1109/EMBC.2014.6944961.
[33]	C. Barka, D. J. Levendowski, M. N. Lumicao , et al., ”EEG correlates of task engagement and mental workload in vigilance, learning, and memory task,” Aviation Space, and Environment Medicine, vol. 78, no. supplement 1, pp. B231-B244, May. 2005.

Emotion Judgment System by EEG Based on Concept Base of EEG Features

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 33

相关文章 0

编辑推荐

Metrics