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Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition

Year 2018, Volume: 18 Issue: 2, 263 - 274, 03.08.2018

Abstract

DOI: 10.26650/electrica.2018.00998

Emotion state detection or emotion
recognition cuts across different disciplines because of the many parameters
that embrace the brain's complex neural structure, signal processing methods,
and pattern recognition algorithms. Currently, in addition to classical
time-frequency methods, emotional state data have been processed via
data-driven methods such as empirical mode decomposition (EMD). Despite its
various benefits, EMD has several drawbacks: it is intended for univariate
data; it is prone to mode mixing; and the number of local extrema must be
enough before the EMD process can begin. To overcome these problems, this study
employs a multivariate EMD and its noise-assisted version in the emotional
state classification of electroencephalogram signals.

References

  • 1. P. Ekman, W. V. Friesen, M. O’Sullivan, A. Chan, I. Diacoyanni-Tarlatzis, K. Heider, R. Krause, W. A. LeCompte, T. Pitcairn, P. E. Ricci-Bitti, “Universals and cultural differences in the judgments of facial expressions of emotion”, J Pers Soc Psychol , vol. 53, no. 4, pp. 712-717, 1987
  • 2. I. Bakker, T. V. der Voordt , P. Vink, J. de Boon, “Pleasure, Arousal, Dominance: Mehrabian revisited”, Curr Psychol, vol 33, no. 3, pp. 405-421, 2014.
  • 3. M. Othman, A. Wahab, I. Karim, M. A. Dzulkifli, I. F. T. Alshaiklia, “EEG emotion recognition based on the dimensional models of emotions”, Procedia-Social and Behavioral Sciences , vol 97, pp. 30-37, 2013.
  • 4. G. K. Verma, U. S. Tiwary, “Affect representation and recognition in 3D continuous valence-arousal-dominance space”, Multimedya Tools Appl, vol. 76, no. 2, pp. 2159-2183, 2016.
  • 5. Y. Liu, O. Sourina, “EEG Databases for Emotion Recognition”, International Conference on Cyberworlds, 2013.
  • 6. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu,H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, H. H. Liu, “The emprical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series anaylsis”, Proc Math Phys Eng Sci, vol. 454, NO. 1971, pp. 903-995, 1998.
  • 7. N. E. Huang, Z. Wu, S. R. Long, K. C. Arnold, X. Chen, K. Blank, “On instantaneous frequency”, Advanced Adaptive Data Anaysis , vol. 1, no. 2, pp. 177-229, 2009.
  • 8. A. Khasnobish, S. Bhattacharyya, G. Singh, A. Jati, A. Konar, D. N. Tibarewala, R. Janarthanan, “The role of Emprical Mode Decomposıtıon on Emotion Classification Using Stimulated EEG signals”, Advances in Computing and Information Technology , vol. 178, pp. 55-62, 2013.
  • 9. A. Santillan-Guzman, M. Fischer, U. Heute, G. Schmidt, “Real-time Empirical Mode Decomposition for EEG signal enhancement”, 21st European Signal Processing Conference (EUSIPCO 2013), 2013.
  • 10. P. C. Petrantonakis, L. J. Hadjileontiadis, “EEG-based emotion recognition using hybrid filtering and higher order crossings”, 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 2009.
  • 11. P. C. Petrantonakis, L. J. Hadjileontiadis, “Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis”, IEEE Transactions on Affective Computing, vol. 1, no. 2, pp. 81-97, 2010.
  • 12. P. C. Petrantonakis, L. J. Hadjileontiadis, “Adaptive Emotional Information Retrieval From EEG Signals in the Time-Frequency Domain”, IEEE Transactions on Signal Processing, vol. 60, NO. 5, pp. 2604-2616, 2012.
  • 13. C. Shahnaz, S. B. Masud, S. M. S. Hasan, “Emotion recognition based on wavelet analysis of Empirical Mode Decomposed EEG signals responsive to music videos”, IEEE Region 10 Conference (TENCON), 2016.
  • 14. Y. Zhang, X. Ji, S. Zhang, “ An approach to EEG-based emotion recognition using combined feature extraction method”, Neurosci Lett, vol. 633, pp. 152-157, 2016.
  • 15. M. S. Hosseini, A. Pouyan, S. Ferdowsi, H. Mashayekhi,”EEG-Based Emotion Recognition using Deep Belief Network and Empirical Mode Decomposition”, 3rd National Conference on Applied Research in Electrical, Mechanical and Mechatronic, 2015.
  • 16. T. M. Rutkowski, A. Cichocki, A. L. Ralescu, D. P. Mandic, “Emotional States Estimation from Multichannel EEG Maps”, Advances in Cognitive Neurodynamics ICCN 2007, 2008, pp. 695-698.
  • 17. V. Bajaj, R. B. Pachori, “EEG Signal Classification Using Empirical Mode Decomposition and Support Vector Machine”, International Conference on Soft Computing for Problem Solving, 2011.
  • 18. P. Ozel, A. Akan, B. Yılmaz, “Emotional State Analysis from EEG signals via Noise-Assisted Multivariate Empirical Mode Decomposition Method”, 10th International Conference on Electrical and Electronics Engineering, 2017.
  • 19. H. Xu, “Towards Automated Recognition of Human Emotion using EEG”, Thesis, Toronto, Canada: University of Toronto, 2012.
  • 20. Y. Tonoyan, D. Looney, D. P. Mandic, M. M. Van Hulle, “Discriminating Multiple Emotional States from EEG Using a Data-Adaptive, Multiscale Information-Theoretic Approach”, Int J Neural Syst, vol. 26, no. 2, p. 1650005, 2016.
  • 21. A. Mert, A.Akan, “Emotion recognition from EEG signals by using multivariate empirical mode decomposition”, Pattern Analysis and Applications, vol. 21, no. 1, pp. 81-89, 2016.
  • 22. C. Guitton, “Emotions Estimation from EEG Recordings”, London: Imperial Collage of Science, Technology & Medicine Department of Electrical&Electronic Engineering, 2010.
  • 23. H. T. Wu, P. Flandrin, I. Daubeschies, “One or two frequencies? The Synchrosqueezing Answers”, Advances in Adaptive Data Anaylsis, vol. 3, no. 01n02, pp. 29-39, 2011.
  • 24. N. U. Rehman, C. Park, N. E. Huang, D. P. Mandic, “Emd via Memd:Multivariate Noise Aided Computation of Standard Emd” Advances in Adaptive Data Analysis, vol. 5, no. 2, p. 1350007, 2013.
  • 25. A. Ahrabian, D. Looney, F. A. Tobar, J. Hallatt, D. P. Mandic, “Noise Assisted Multivariate Empirical Mode Decomposition Applied to Doppler Radar Data”, Sensor Signal Processing for Defence (SSPD 2012), 2012.
  • 26. Q. S. She, Y. L. Ma, M. Meng, X. G. Xi, Z. Z. Luo, “Noise-assisted MEMD based relevant IMFs identification and EEG classification”, Journal of Central South University , vol. 24, no. 3, p. 599-608, 2017.
  • 27. J. Alegre-Cortés, S. Sánchez, A. G. Pizá, A. L. Albarracín, F. D. Farfán, C. J. Felice, E. Fernández, “Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition”, J Neuroscie Methods, vol. 267, pp. 35-44, 2016.
  • 28. D. Cho, B. Min, J. Kim, B. Lee, “EEG-based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition”, IEEE Transs Neural Syst Rehabil Eng, vol. 25, no. 8, pp. 1309-1318, 2017.
  • 29. Lin,C.S, Tanumihardja, W.A., Shih,H.H., “Lung-heart sound separation using noise assisted multivariate empirical mode decomposition”, International Symposium on Intelligent Signal Processing and Communication Systems, 2013.
  • 30. Y. Zhang, P. Xu, P. Li, K. Duan, Y. Wen, Q. Yang, T. Zhang, D. Yao, “Noise Assisted Multivariate Empirical Mode Decomposition for Multichannel EMG Signals,” BioMedical Engineering Online, vol. 16, no. 107, pp. 1-17, 2017.
  • 31. S. Sungkono, B. J. Santosa, A. S. Bahri, F. M. Santos, A. Iswahyudi, “Application of Noise Assisted Multivariate Empirical Mode Decomposition in VLF-EM Data to Identify Underground River”, Advances in Data Science and Adaptive Analysis, vol. 9, no. 1, p. 1650011, 2017.
  • 32. W. Huang, J. Zeng, Z. Wang, J. Liang, “Partial noise assisted multivariate EMD:An improved noise assisted method for multivariate signals decomposition”, Biomedical Signal Processing and Control, vol. 36, pp. 205-220, 2017.
  • 33. S. Koelstra, C. Muhl, M. Soleymani, J. S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, I. Patras, “DEAP: A database for emotion analysis; using physiological signals”, IEEE transactions on affective computing, vol. 3, no. 1, pp. 18-31, 2012.
  • 34. N. Rehman, D. P. Mandic, “Multivariate empirical mode decomposition”, Proc Math Phys Eng Sci, vol. 466, no. 2117, pp. 1291-1302, 2009.
  • 35. P. Flandrin, P. Gonçalves, G. Rilling, “EMD equivalent filter banks, from interpetation”, Hilbert-Huang Transform and Its Applications, Singapore , World Scientific, 2005, pp. 67-87.
  • 36. Z. Wu, N. E. Huang, “Ensemble Emprical Mode Decomposition : A Noise Assisted Data Analysis Method”, Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1-41, 2009.
  • 37. M. E. Torres, M. A. Colominas, G. Schlotthauer, P. Flandrin, “A complete ensemble empirical mode decomposition with adaptive noise”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Prague, 2011.
  • 38. J. R. Yeh, J. S. Shieh, N. E. Huang, “Complementary ensemble empirical mode”, Advanced Adaptive Data Anaylsis, vol. 2, no. 2, pp. 135-156, 2010.
  • 39. Hao, H., Wang, H., Rehman, N.U., Tian, H., “A Study of the Characteristics of MEMD for Fractional Gaussian Noise” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol E99.A, no. 6, pp. 1228-1232, 2016.
  • 40. S. Ge, Y. H. Shi, R. M. Wang, P. Lin, J. F. Gao, G. P. Sun, K. Iramina, Y. K. Yang, Y. Leng, H. Wang, W. Zheng, “Sinusoidal Signal Assisted Multivariate Empirical Mode Decomposition for Brain-Computer Interfaces”, IEEE Journal of Biomedical and Health Informatics , 2017.
  • 41. N. ur Rehman, D. P. Mandic, “Filter bank property of multivariate empirical mode decomposition”, IEEE transactions on signal processing, vol. 59, no. 5 , pp. 2421-2426., 2011.

Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition

Year 2018, Volume: 18 Issue: 2, 263 - 274, 03.08.2018

Abstract

DOI: 10.26650/electrica.2018.00998

Emotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals.

References

  • 1. P. Ekman, W. V. Friesen, M. O’Sullivan, A. Chan, I. Diacoyanni-Tarlatzis, K. Heider, R. Krause, W. A. LeCompte, T. Pitcairn, P. E. Ricci-Bitti, “Universals and cultural differences in the judgments of facial expressions of emotion”, J Pers Soc Psychol , vol. 53, no. 4, pp. 712-717, 1987
  • 2. I. Bakker, T. V. der Voordt , P. Vink, J. de Boon, “Pleasure, Arousal, Dominance: Mehrabian revisited”, Curr Psychol, vol 33, no. 3, pp. 405-421, 2014.
  • 3. M. Othman, A. Wahab, I. Karim, M. A. Dzulkifli, I. F. T. Alshaiklia, “EEG emotion recognition based on the dimensional models of emotions”, Procedia-Social and Behavioral Sciences , vol 97, pp. 30-37, 2013.
  • 4. G. K. Verma, U. S. Tiwary, “Affect representation and recognition in 3D continuous valence-arousal-dominance space”, Multimedya Tools Appl, vol. 76, no. 2, pp. 2159-2183, 2016.
  • 5. Y. Liu, O. Sourina, “EEG Databases for Emotion Recognition”, International Conference on Cyberworlds, 2013.
  • 6. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu,H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, H. H. Liu, “The emprical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series anaylsis”, Proc Math Phys Eng Sci, vol. 454, NO. 1971, pp. 903-995, 1998.
  • 7. N. E. Huang, Z. Wu, S. R. Long, K. C. Arnold, X. Chen, K. Blank, “On instantaneous frequency”, Advanced Adaptive Data Anaysis , vol. 1, no. 2, pp. 177-229, 2009.
  • 8. A. Khasnobish, S. Bhattacharyya, G. Singh, A. Jati, A. Konar, D. N. Tibarewala, R. Janarthanan, “The role of Emprical Mode Decomposıtıon on Emotion Classification Using Stimulated EEG signals”, Advances in Computing and Information Technology , vol. 178, pp. 55-62, 2013.
  • 9. A. Santillan-Guzman, M. Fischer, U. Heute, G. Schmidt, “Real-time Empirical Mode Decomposition for EEG signal enhancement”, 21st European Signal Processing Conference (EUSIPCO 2013), 2013.
  • 10. P. C. Petrantonakis, L. J. Hadjileontiadis, “EEG-based emotion recognition using hybrid filtering and higher order crossings”, 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 2009.
  • 11. P. C. Petrantonakis, L. J. Hadjileontiadis, “Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis”, IEEE Transactions on Affective Computing, vol. 1, no. 2, pp. 81-97, 2010.
  • 12. P. C. Petrantonakis, L. J. Hadjileontiadis, “Adaptive Emotional Information Retrieval From EEG Signals in the Time-Frequency Domain”, IEEE Transactions on Signal Processing, vol. 60, NO. 5, pp. 2604-2616, 2012.
  • 13. C. Shahnaz, S. B. Masud, S. M. S. Hasan, “Emotion recognition based on wavelet analysis of Empirical Mode Decomposed EEG signals responsive to music videos”, IEEE Region 10 Conference (TENCON), 2016.
  • 14. Y. Zhang, X. Ji, S. Zhang, “ An approach to EEG-based emotion recognition using combined feature extraction method”, Neurosci Lett, vol. 633, pp. 152-157, 2016.
  • 15. M. S. Hosseini, A. Pouyan, S. Ferdowsi, H. Mashayekhi,”EEG-Based Emotion Recognition using Deep Belief Network and Empirical Mode Decomposition”, 3rd National Conference on Applied Research in Electrical, Mechanical and Mechatronic, 2015.
  • 16. T. M. Rutkowski, A. Cichocki, A. L. Ralescu, D. P. Mandic, “Emotional States Estimation from Multichannel EEG Maps”, Advances in Cognitive Neurodynamics ICCN 2007, 2008, pp. 695-698.
  • 17. V. Bajaj, R. B. Pachori, “EEG Signal Classification Using Empirical Mode Decomposition and Support Vector Machine”, International Conference on Soft Computing for Problem Solving, 2011.
  • 18. P. Ozel, A. Akan, B. Yılmaz, “Emotional State Analysis from EEG signals via Noise-Assisted Multivariate Empirical Mode Decomposition Method”, 10th International Conference on Electrical and Electronics Engineering, 2017.
  • 19. H. Xu, “Towards Automated Recognition of Human Emotion using EEG”, Thesis, Toronto, Canada: University of Toronto, 2012.
  • 20. Y. Tonoyan, D. Looney, D. P. Mandic, M. M. Van Hulle, “Discriminating Multiple Emotional States from EEG Using a Data-Adaptive, Multiscale Information-Theoretic Approach”, Int J Neural Syst, vol. 26, no. 2, p. 1650005, 2016.
  • 21. A. Mert, A.Akan, “Emotion recognition from EEG signals by using multivariate empirical mode decomposition”, Pattern Analysis and Applications, vol. 21, no. 1, pp. 81-89, 2016.
  • 22. C. Guitton, “Emotions Estimation from EEG Recordings”, London: Imperial Collage of Science, Technology & Medicine Department of Electrical&Electronic Engineering, 2010.
  • 23. H. T. Wu, P. Flandrin, I. Daubeschies, “One or two frequencies? The Synchrosqueezing Answers”, Advances in Adaptive Data Anaylsis, vol. 3, no. 01n02, pp. 29-39, 2011.
  • 24. N. U. Rehman, C. Park, N. E. Huang, D. P. Mandic, “Emd via Memd:Multivariate Noise Aided Computation of Standard Emd” Advances in Adaptive Data Analysis, vol. 5, no. 2, p. 1350007, 2013.
  • 25. A. Ahrabian, D. Looney, F. A. Tobar, J. Hallatt, D. P. Mandic, “Noise Assisted Multivariate Empirical Mode Decomposition Applied to Doppler Radar Data”, Sensor Signal Processing for Defence (SSPD 2012), 2012.
  • 26. Q. S. She, Y. L. Ma, M. Meng, X. G. Xi, Z. Z. Luo, “Noise-assisted MEMD based relevant IMFs identification and EEG classification”, Journal of Central South University , vol. 24, no. 3, p. 599-608, 2017.
  • 27. J. Alegre-Cortés, S. Sánchez, A. G. Pizá, A. L. Albarracín, F. D. Farfán, C. J. Felice, E. Fernández, “Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition”, J Neuroscie Methods, vol. 267, pp. 35-44, 2016.
  • 28. D. Cho, B. Min, J. Kim, B. Lee, “EEG-based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition”, IEEE Transs Neural Syst Rehabil Eng, vol. 25, no. 8, pp. 1309-1318, 2017.
  • 29. Lin,C.S, Tanumihardja, W.A., Shih,H.H., “Lung-heart sound separation using noise assisted multivariate empirical mode decomposition”, International Symposium on Intelligent Signal Processing and Communication Systems, 2013.
  • 30. Y. Zhang, P. Xu, P. Li, K. Duan, Y. Wen, Q. Yang, T. Zhang, D. Yao, “Noise Assisted Multivariate Empirical Mode Decomposition for Multichannel EMG Signals,” BioMedical Engineering Online, vol. 16, no. 107, pp. 1-17, 2017.
  • 31. S. Sungkono, B. J. Santosa, A. S. Bahri, F. M. Santos, A. Iswahyudi, “Application of Noise Assisted Multivariate Empirical Mode Decomposition in VLF-EM Data to Identify Underground River”, Advances in Data Science and Adaptive Analysis, vol. 9, no. 1, p. 1650011, 2017.
  • 32. W. Huang, J. Zeng, Z. Wang, J. Liang, “Partial noise assisted multivariate EMD:An improved noise assisted method for multivariate signals decomposition”, Biomedical Signal Processing and Control, vol. 36, pp. 205-220, 2017.
  • 33. S. Koelstra, C. Muhl, M. Soleymani, J. S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, I. Patras, “DEAP: A database for emotion analysis; using physiological signals”, IEEE transactions on affective computing, vol. 3, no. 1, pp. 18-31, 2012.
  • 34. N. Rehman, D. P. Mandic, “Multivariate empirical mode decomposition”, Proc Math Phys Eng Sci, vol. 466, no. 2117, pp. 1291-1302, 2009.
  • 35. P. Flandrin, P. Gonçalves, G. Rilling, “EMD equivalent filter banks, from interpetation”, Hilbert-Huang Transform and Its Applications, Singapore , World Scientific, 2005, pp. 67-87.
  • 36. Z. Wu, N. E. Huang, “Ensemble Emprical Mode Decomposition : A Noise Assisted Data Analysis Method”, Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1-41, 2009.
  • 37. M. E. Torres, M. A. Colominas, G. Schlotthauer, P. Flandrin, “A complete ensemble empirical mode decomposition with adaptive noise”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Prague, 2011.
  • 38. J. R. Yeh, J. S. Shieh, N. E. Huang, “Complementary ensemble empirical mode”, Advanced Adaptive Data Anaylsis, vol. 2, no. 2, pp. 135-156, 2010.
  • 39. Hao, H., Wang, H., Rehman, N.U., Tian, H., “A Study of the Characteristics of MEMD for Fractional Gaussian Noise” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol E99.A, no. 6, pp. 1228-1232, 2016.
  • 40. S. Ge, Y. H. Shi, R. M. Wang, P. Lin, J. F. Gao, G. P. Sun, K. Iramina, Y. K. Yang, Y. Leng, H. Wang, W. Zheng, “Sinusoidal Signal Assisted Multivariate Empirical Mode Decomposition for Brain-Computer Interfaces”, IEEE Journal of Biomedical and Health Informatics , 2017.
  • 41. N. ur Rehman, D. P. Mandic, “Filter bank property of multivariate empirical mode decomposition”, IEEE transactions on signal processing, vol. 59, no. 5 , pp. 2421-2426., 2011.
There are 41 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Pınar Özel

Aydın Akan This is me

Bülent Yılmaz This is me

Publication Date August 3, 2018
Published in Issue Year 2018 Volume: 18 Issue: 2

Cite

APA Özel, P., Akan, A., & Yılmaz, B. (2018). Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition. Electrica, 18(2), 263-274.
AMA Özel P, Akan A, Yılmaz B. Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition. Electrica. August 2018;18(2):263-274.
Chicago Özel, Pınar, Aydın Akan, and Bülent Yılmaz. “Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition”. Electrica 18, no. 2 (August 2018): 263-74.
EndNote Özel P, Akan A, Yılmaz B (August 1, 2018) Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition. Electrica 18 2 263–274.
IEEE P. Özel, A. Akan, and B. Yılmaz, “Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition”, Electrica, vol. 18, no. 2, pp. 263–274, 2018.
ISNAD Özel, Pınar et al. “Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition”. Electrica 18/2 (August 2018), 263-274.
JAMA Özel P, Akan A, Yılmaz B. Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition. Electrica. 2018;18:263–274.
MLA Özel, Pınar et al. “Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition”. Electrica, vol. 18, no. 2, 2018, pp. 263-74.
Vancouver Özel P, Akan A, Yılmaz B. Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition. Electrica. 2018;18(2):263-74.