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EEG Tabanlı Duygu Analizi Sistemleri

Year 2018, Volume: 11 Issue: 1, 26 - 39, 05.06.2018

Talha Burak Alakuş , İbrahim Türkoğlu

Abstract

Duygu tahmini, insan-makine arasındaki iletişimi
arttırmak ve kontrol edebilmek için beyin-bilgisayar arayüzü, sağlık hizmeti,
güvenlik, e-ticaret, eğitim ve eğlence uygulamalarında kullanılmaktadır. Ancak
duyguların soyut olması, kişiden kişiye değişiklik göstermesi, evrensel bir
tanımının olmaması ve duyguların çok sayıda iç ve dış etkilere açık olması, bu
alanda yapılan çalışmaları zorlaştırmaktadır. Son zamanlarda duygu analizi
işleminin sağlıklı olabilmesi için başvurulan en yaygın yöntem olan beyin sinyallerine
dayalı duygu tahmini araştırmaları hız kazanmıştır. Bu makalede EEG’ye bağlı
olarak yapılan duygu tahmini çalışmaları irdelenmiştir. Çalışmalarda kullanılan
EEG verileri, duygu uyaranları, kullanılan analiz yöntemleri incelenmiş ve
sınıflandırma başarımları karşılaştırılmıştır.




Keywords

Duygu EEG Sınıfla Sinyal İşleme

References

  • [1] Frantzidis, C.A., Bratsas, C., Klados, M.A., Konstandinidis, E., Lithari, C.D, Vivas, A.B., Papadelis, C.L., Kaldoudi, E., Pappas, C., Bamidis, P.D., On the Classification of Emotional Biosignals Evoked While Viewing Affective Pictures: An Integrated Data-Mining-Based Approach for Healthcare Applications, IEEE Trans. Information Tech. Biomedicine, 2010, pp. 309-318.
  • [2] Petrushin, V., Emotion in Speech: Recognition and Application to Call, Proceedings of Artificial Neural Networks in Engineering Conference, 1999, pp.7-10.
  • [3] Black, M., Yacoob, Y., Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion, International Journal of Computer Visions 25, 1997, pp.23-48.
  • [4] Szwoch, W., Using Physiological Signals for Emotion, Sopot, Poland, 2013.
  • [5] Cooper, R., Osselton, J.W., Shaw, J.C., EEG Technology, 1969.
  • [6] Jasper, H.H., The Ten-Twenty Electrode System of the International Federation, Electroencephalography and Clinical Neurophysiology, 10, 1958, pp.371-375.
  • [7] Koelstra, S., Mühl, C., Soleymani, M., DEAP: A Database for Emotion Analysis: Using Physiological Signals, IEEE Transactions on Affective Computing, 3(1), 2012, pp. 18-31.
  • [8] Wichakam, I., Vateekul, P., An Evaluation of Feature Extraction in EEG-Based Emotion Prediction with Support Vector Machines, Proceedings of the 2014 11th International Joint Conference on Computer Science and Software Engineering, 2014, pp.106-110.
  • [9] Ekman, P., An Argumant for Basic Emotions, Cognition and Emotion 6(3), 1992, pp. 169-200.
  • [10] Russel, J. A., Core Affect and Psychological Construction of Emotion, Psychoogical Review, 110(1), 2003, pp.145-150.
  • [11] Russel, J.A., Culture and the Categorization of Emotions, Psychological Bulletin, 110, 1991, pp. 425-450.
  • [12] Jirayucharoensak, S., Pangum, S., Israsena, P., EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation, The Scientific World Journal, 2014.
  • [13] Lang, P.J., Bradley, M.M., Cuthbert, B.N., International Affective Picture System (IAPS): Technical Manual and Affective Rating, NIMH Center for the Study of Emotion and Attention, 1997.
  • [14] Liu, Y.H., Cheng, W.T., Hsiao, Y.T., EEG-Based Emotion Recognition Based on Kernel Fisher’s Discriminant Analysis and Spectral Powers, 2014 IEEE International Conference on Systems, Man and Cybernetics, 2014, pp. 2221-2225.
  • [15] Paul, S., Mazumder, A., Ghosh, P., Tibarewala, D.N., Vimalarani, G., EEG Based Emotion Recognition System using MFDFA as Feature Extractor, International Conference on Robotics, Automation, Control and Embedded Systems, 2015.
  • [16] Naji, M., Firoozabadi, M., Azadfallah, P., Emotion Classification During Music Listening from Forehead Biosignals, Signal, Image and Video Processing, 9(6), 2015, pp. 1365-1375.
  • [17] Bajaj, V., Pachori, R.B., Detection of Human Emotions using Features Based on the Multiwavelet Transform of EEG Signals, Brain Computer Interfaces, 2015, pp. 215-240.
  • [18] Lahane, P., Sangaiah, A.K., An Approach to EEG Based Emotion Recognition and Classification using Kernel Density Estimation, Procedia Computer Science, 48, 2015, pp. 574-581.
  • [19] Javaid, M.M., Yousaf, M.A., Real-Time EEG-Based Human Emotion Recognition, Neural Information Processing, 2015, pp. 182-190.
  • [20] Koelstra, S., Muhl, C., Soleymani, M., DEAP: A Database for Emotion Analysis using Physiological Signals, Affective Computing, 3, 2012, pp. 18-31.
  • [21] Xin, L., Xiao-Ying, Q., Xiao-Qi, S., Xiao-Feng, S., Relevance Vector Machine Based EEG Emotion Recognition, Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control, 2016, pp. 293-297.
  • [22] Tabanfar, Z., Yousefpoor, F., Firoozabadi, M., Khodakarami, Z., Recognition of Two Emotional States of Joy and Sadness using Phase Lag Index and SVM Classifier, 23rd Iranian Conference on Biomedical Engineering and 1st International Iranian Conference on Biomedical Engineering, 2016.
  • [23] Mehmood, R.M., Lee, H. J., Emotion Recognition from EEG Brain Signals Based on Particle Swarm Optimization and Genetic Search, IEEE International Conference on Multimedia & Expo Workshops, 2016.
  • [24] Ackermann, P., Kohlschein, C., Bitsch, J. A., Wehrle, K., Jeschke, S., EEG-Based Automatic Emotion Recognition: Feature Extraction, Selection and Classification Methods, IEEE 18th International Conference on e-Health Networking, Applications and Services, 2016.
  • [25] Gomez, A., Quientero, L., Lopez, N., Castro, J., Villa, L., Mejia, G., An Approach to Emotion Recognition in Single-Channel EEG Signals using Stationary Wavelet Transform, VII Latin American Congress on Biomedical Engineering, 2016, pp. 654-657.
  • [26] Pan, J., Li, Y., Wang, J., An EEG-Based Brain-Computer Interface for Emotion Recognition, 2016 International Conference on Neural Networks, 2016, pp. 2063-2067.
  • [27] Ghare, P. S., Paithane, A. N., Human Emotion Recognition using Non Linear and Non Stationary EEG Signal, International Conference on Automatic Control and Dynamic Optimization Techniques, 2016, pp. 1013-1016.
  • [28] Patil, A., Deshmukh, C., Panat, A.R., Feature Extraction of EEG for Emotion Recognition using Hjorth Features and Higher Order Crossings, Conference on Advances in Signal Processing, 2016, pp. 429-434.
  • [29] Ralekar, C., Gandhi, T.K., Roy, S.K., Emotion Classification from EEG Signals, 3rd International Conference on Computing for Sustainable Global Development, 2016, pp. 2543-2546.
  • [30] Zhang, Y., Ji, X., Zhang, S., An Approach to EEG-Based Emotion Reognition using Combined Feature Extraction Method, Neuroscience Letters, 644, 2016, pp. 152-157.
  • [31] Mangalagowri, S.G., Cyrill, P.R.P., EEG Feature Extraction and Classification using Feed Forward Backpropagation Algorithm for Emotion Detection, 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques, 2016, pp. 183-187.
  • [32] Shahnaz, C., Bin-Masud, S., Hasan, M.S., Emotion Recognition Based on Wavelet Analysis of Emprical Mode Decomposed EEG Signals Responsive to Music Videos, Region10 Conference, 2016, pp. 424-427.
  • [33] Kumar, N., Khaund, K., Hazarika, S.M., Bispectral Analysis of EEG for Emotion Recognition, Procedia Computer Science, 84, 2016, pp. 31-35.
  • [34] Zhuang, N., Zeng, Y., Tong, L., Zhang, C., Zhang, H., Yan, B., Emotion Recognition from EEG Signals using Multidimensional Information in EMD Domain, BioMed Research International, 2017.
  • [35] Özerdem, M.S., Polat, H., Emotion Recognition Based on EEG Features in Movie Clips with Channel Selection, Brain Informatics, 2017, pp. 1-12.
  • [36] Mohammadpour, M., Hashemi, S.M.R., Houshmand, N., Classification of EEG-Based Emotion for BCI Applications, Artificial Intelligence and Robotics, 2017, pp. 127-131.
  • [37] Michalopoulo, K., Bourbakis, N., Application of Multiscale Entropy on EEG Signals for Emotion Detection, International Conference on Biomedical and Health Informatics, 2017, pp. 341-344.
  • [38] Candra, H., Yuwono, M., Chai, R., Nguyen, H.T., Su, S., EEG Emotion Recognition using Reduced Channel Wavelet Entropy and Average Wavelet Coefficient Features with Normal Mutual Information Method, 39th Annual International Conference of the Engineering in Medicine and Biology Society, 2017, pp. 463-466.
  • [39] Krisnandhika, B., Faqig, A., Purnamasari, P.W., Kusumoputro, B., Emotion Recognition System Based on EEG Signals using Relative Wavelet Energy Features and a Modified Radial Basis Function Neural Networks, International Conference on Consumer Electronics and Devices, 2017, pp. 50-54.
  • [40] Sarıkaya, M.A., İnce, G., Emotion Recognition from EEG Signals Through One Electrode Device, Signal Processing and Communications Applications Conference, 2017.
  • [41] Ang, A.Q-X., Yeong, T.Q., Ser, W., Emotion Classification from EEG Signals Using Time-Frequency –DWT Features and ANN, Journal of Computer anc Communication, 5, 2017, pp. 75-79.
  • [42] Turnip, A., Simbolon, A.I., Amri, M.F., Sihombing, P., Setiadi, R.H., Mulyana, E., Backpropagation Neural Networks Training for EEG-SSVEP Classification of Emotion Recognition, Internetworking Indonesia Journal, 9(1), 2017, pp.53-57.

Year 2018, Volume: 11 Issue: 1, 26 - 39, 05.06.2018

Talha Burak Alakuş , İbrahim Türkoğlu

Abstract

References

  • [1] Frantzidis, C.A., Bratsas, C., Klados, M.A., Konstandinidis, E., Lithari, C.D, Vivas, A.B., Papadelis, C.L., Kaldoudi, E., Pappas, C., Bamidis, P.D., On the Classification of Emotional Biosignals Evoked While Viewing Affective Pictures: An Integrated Data-Mining-Based Approach for Healthcare Applications, IEEE Trans. Information Tech. Biomedicine, 2010, pp. 309-318.
  • [2] Petrushin, V., Emotion in Speech: Recognition and Application to Call, Proceedings of Artificial Neural Networks in Engineering Conference, 1999, pp.7-10.
  • [3] Black, M., Yacoob, Y., Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion, International Journal of Computer Visions 25, 1997, pp.23-48.
  • [4] Szwoch, W., Using Physiological Signals for Emotion, Sopot, Poland, 2013.
  • [5] Cooper, R., Osselton, J.W., Shaw, J.C., EEG Technology, 1969.
  • [6] Jasper, H.H., The Ten-Twenty Electrode System of the International Federation, Electroencephalography and Clinical Neurophysiology, 10, 1958, pp.371-375.
  • [7] Koelstra, S., Mühl, C., Soleymani, M., DEAP: A Database for Emotion Analysis: Using Physiological Signals, IEEE Transactions on Affective Computing, 3(1), 2012, pp. 18-31.
  • [8] Wichakam, I., Vateekul, P., An Evaluation of Feature Extraction in EEG-Based Emotion Prediction with Support Vector Machines, Proceedings of the 2014 11th International Joint Conference on Computer Science and Software Engineering, 2014, pp.106-110.
  • [9] Ekman, P., An Argumant for Basic Emotions, Cognition and Emotion 6(3), 1992, pp. 169-200.
  • [10] Russel, J. A., Core Affect and Psychological Construction of Emotion, Psychoogical Review, 110(1), 2003, pp.145-150.
  • [11] Russel, J.A., Culture and the Categorization of Emotions, Psychological Bulletin, 110, 1991, pp. 425-450.
  • [12] Jirayucharoensak, S., Pangum, S., Israsena, P., EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation, The Scientific World Journal, 2014.
  • [13] Lang, P.J., Bradley, M.M., Cuthbert, B.N., International Affective Picture System (IAPS): Technical Manual and Affective Rating, NIMH Center for the Study of Emotion and Attention, 1997.
  • [14] Liu, Y.H., Cheng, W.T., Hsiao, Y.T., EEG-Based Emotion Recognition Based on Kernel Fisher’s Discriminant Analysis and Spectral Powers, 2014 IEEE International Conference on Systems, Man and Cybernetics, 2014, pp. 2221-2225.
  • [15] Paul, S., Mazumder, A., Ghosh, P., Tibarewala, D.N., Vimalarani, G., EEG Based Emotion Recognition System using MFDFA as Feature Extractor, International Conference on Robotics, Automation, Control and Embedded Systems, 2015.
  • [16] Naji, M., Firoozabadi, M., Azadfallah, P., Emotion Classification During Music Listening from Forehead Biosignals, Signal, Image and Video Processing, 9(6), 2015, pp. 1365-1375.
  • [17] Bajaj, V., Pachori, R.B., Detection of Human Emotions using Features Based on the Multiwavelet Transform of EEG Signals, Brain Computer Interfaces, 2015, pp. 215-240.
  • [18] Lahane, P., Sangaiah, A.K., An Approach to EEG Based Emotion Recognition and Classification using Kernel Density Estimation, Procedia Computer Science, 48, 2015, pp. 574-581.
  • [19] Javaid, M.M., Yousaf, M.A., Real-Time EEG-Based Human Emotion Recognition, Neural Information Processing, 2015, pp. 182-190.
  • [20] Koelstra, S., Muhl, C., Soleymani, M., DEAP: A Database for Emotion Analysis using Physiological Signals, Affective Computing, 3, 2012, pp. 18-31.
  • [21] Xin, L., Xiao-Ying, Q., Xiao-Qi, S., Xiao-Feng, S., Relevance Vector Machine Based EEG Emotion Recognition, Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control, 2016, pp. 293-297.
  • [22] Tabanfar, Z., Yousefpoor, F., Firoozabadi, M., Khodakarami, Z., Recognition of Two Emotional States of Joy and Sadness using Phase Lag Index and SVM Classifier, 23rd Iranian Conference on Biomedical Engineering and 1st International Iranian Conference on Biomedical Engineering, 2016.
  • [23] Mehmood, R.M., Lee, H. J., Emotion Recognition from EEG Brain Signals Based on Particle Swarm Optimization and Genetic Search, IEEE International Conference on Multimedia & Expo Workshops, 2016.
  • [24] Ackermann, P., Kohlschein, C., Bitsch, J. A., Wehrle, K., Jeschke, S., EEG-Based Automatic Emotion Recognition: Feature Extraction, Selection and Classification Methods, IEEE 18th International Conference on e-Health Networking, Applications and Services, 2016.
  • [25] Gomez, A., Quientero, L., Lopez, N., Castro, J., Villa, L., Mejia, G., An Approach to Emotion Recognition in Single-Channel EEG Signals using Stationary Wavelet Transform, VII Latin American Congress on Biomedical Engineering, 2016, pp. 654-657.
  • [26] Pan, J., Li, Y., Wang, J., An EEG-Based Brain-Computer Interface for Emotion Recognition, 2016 International Conference on Neural Networks, 2016, pp. 2063-2067.
  • [27] Ghare, P. S., Paithane, A. N., Human Emotion Recognition using Non Linear and Non Stationary EEG Signal, International Conference on Automatic Control and Dynamic Optimization Techniques, 2016, pp. 1013-1016.
  • [28] Patil, A., Deshmukh, C., Panat, A.R., Feature Extraction of EEG for Emotion Recognition using Hjorth Features and Higher Order Crossings, Conference on Advances in Signal Processing, 2016, pp. 429-434.
  • [29] Ralekar, C., Gandhi, T.K., Roy, S.K., Emotion Classification from EEG Signals, 3rd International Conference on Computing for Sustainable Global Development, 2016, pp. 2543-2546.
  • [30] Zhang, Y., Ji, X., Zhang, S., An Approach to EEG-Based Emotion Reognition using Combined Feature Extraction Method, Neuroscience Letters, 644, 2016, pp. 152-157.
  • [31] Mangalagowri, S.G., Cyrill, P.R.P., EEG Feature Extraction and Classification using Feed Forward Backpropagation Algorithm for Emotion Detection, 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques, 2016, pp. 183-187.
  • [32] Shahnaz, C., Bin-Masud, S., Hasan, M.S., Emotion Recognition Based on Wavelet Analysis of Emprical Mode Decomposed EEG Signals Responsive to Music Videos, Region10 Conference, 2016, pp. 424-427.
  • [33] Kumar, N., Khaund, K., Hazarika, S.M., Bispectral Analysis of EEG for Emotion Recognition, Procedia Computer Science, 84, 2016, pp. 31-35.
  • [34] Zhuang, N., Zeng, Y., Tong, L., Zhang, C., Zhang, H., Yan, B., Emotion Recognition from EEG Signals using Multidimensional Information in EMD Domain, BioMed Research International, 2017.
  • [35] Özerdem, M.S., Polat, H., Emotion Recognition Based on EEG Features in Movie Clips with Channel Selection, Brain Informatics, 2017, pp. 1-12.
  • [36] Mohammadpour, M., Hashemi, S.M.R., Houshmand, N., Classification of EEG-Based Emotion for BCI Applications, Artificial Intelligence and Robotics, 2017, pp. 127-131.
  • [37] Michalopoulo, K., Bourbakis, N., Application of Multiscale Entropy on EEG Signals for Emotion Detection, International Conference on Biomedical and Health Informatics, 2017, pp. 341-344.
  • [38] Candra, H., Yuwono, M., Chai, R., Nguyen, H.T., Su, S., EEG Emotion Recognition using Reduced Channel Wavelet Entropy and Average Wavelet Coefficient Features with Normal Mutual Information Method, 39th Annual International Conference of the Engineering in Medicine and Biology Society, 2017, pp. 463-466.
  • [39] Krisnandhika, B., Faqig, A., Purnamasari, P.W., Kusumoputro, B., Emotion Recognition System Based on EEG Signals using Relative Wavelet Energy Features and a Modified Radial Basis Function Neural Networks, International Conference on Consumer Electronics and Devices, 2017, pp. 50-54.
  • [40] Sarıkaya, M.A., İnce, G., Emotion Recognition from EEG Signals Through One Electrode Device, Signal Processing and Communications Applications Conference, 2017.
  • [41] Ang, A.Q-X., Yeong, T.Q., Ser, W., Emotion Classification from EEG Signals Using Time-Frequency –DWT Features and ANN, Journal of Computer anc Communication, 5, 2017, pp. 75-79.
  • [42] Turnip, A., Simbolon, A.I., Amri, M.F., Sihombing, P., Setiadi, R.H., Mulyana, E., Backpropagation Neural Networks Training for EEG-SSVEP Classification of Emotion Recognition, Internetworking Indonesia Journal, 9(1), 2017, pp.53-57.
There are 42 citations in total.

Details

Subjects Engineering
Journal Section Makaleler(Araştırma)
Authors

Talha Burak Alakuş

İbrahim Türkoğlu

Publication Date June 5, 2018
Published in Issue Year 2018 Volume: 11 Issue: 1

Cite

APA Alakuş, T. B., & Türkoğlu, İ. (2018). EEG Tabanlı Duygu Analizi Sistemleri. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, 11(1), 26-39.
AMA Alakuş TB, Türkoğlu İ. EEG Tabanlı Duygu Analizi Sistemleri. TBV-BBMD. June 2018;11(1):26-39.
Chicago Alakuş, Talha Burak, and İbrahim Türkoğlu. “EEG Tabanlı Duygu Analizi Sistemleri”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi 11, no. 1 (June 2018): 26-39.
EndNote Alakuş TB, Türkoğlu İ (June 1, 2018) EEG Tabanlı Duygu Analizi Sistemleri. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 11 1 26–39.
IEEE T. B. Alakuş and İ. Türkoğlu, “EEG Tabanlı Duygu Analizi Sistemleri”, TBV-BBMD, vol. 11, no. 1, pp. 26–39, 2018.
ISNAD Alakuş, Talha Burak - Türkoğlu, İbrahim. “EEG Tabanlı Duygu Analizi Sistemleri”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 11/1 (June 2018), 26-39.
JAMA Alakuş TB, Türkoğlu İ. EEG Tabanlı Duygu Analizi Sistemleri. TBV-BBMD. 2018;11:26–39.
MLA Alakuş, Talha Burak and İbrahim Türkoğlu. “EEG Tabanlı Duygu Analizi Sistemleri”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, vol. 11, no. 1, 2018, pp. 26-39.
Vancouver Alakuş TB, Türkoğlu İ. EEG Tabanlı Duygu Analizi Sistemleri. TBV-BBMD. 2018;11(1):26-39.
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