Human Emotion Detection Based on Machine Learning

Montazer  M. Mohsen; Firas  S. Miften

doi:10.32792/jeps.v11i2.123

المؤلفون

Montazer M. Mohsen 1 University of Thi-Qar College of Education for Pure Science, Computer Science Department, Iraq
Firas S. Miften Ministry of Education, Thi-Qar Education Directorate, Iraq

DOI:

https://doi.org/10.32792/jeps.v11i2.123

الكلمات المفتاحية:

multi-channel EEG، discrete wavelet transform (DWT),، frequency bands

الملخص

Emotion is a mental and physiological state associated with a wide variety of feelings, thoughts and behaviors. Emotions are fundamental in the daily life of human beings as they play an important role in human cognition, namely in rational decision-making, perception, human interaction, and human intelligence. This paper investigates the effect for the emotion-discriminating precision of Different wave levels of EEG signals and a particular number of channels. It using various sets of EEG channels, the proposal classified affective states in the equivalence and excitability dimensions. To begin, DEAP normalized the pretreated hypothetical data. Following that, discrete wavelet transduction was used to divide the EEG into four bands, The scales used were the features of the K-nearest neighbor Algorithm entropy and energy algorithm. The highest classification accuracy was using the K-NN algorithm for channels (4-10-14-18, 32) in the four dimensions (valence, arousal, dominance, and liking). They are channel 18 (99.7656%, 99.7656%, 99.7656%, 99.7656%) respectively. While the highest classification accuracy for the frequency bands is the gamma frequency greater from beta and alpha and theta frequency for the four dimensions is (99.7656%)

المراجع

References: ͳǤ Ang, J., Dhillon, R., Krupski, A., Shriberg, E., & Stolcke, A. (2002). Prosody-based automatic detection of annoyance and frustration in human-computer dialog. Seventh International Conference on Spoken Language Processing. ʹǤ Atkinson, J., & Campos, D. (2016). Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Systems with Applications, 47, 35– 41. ͵Ǥ Azeez, R. A., Miften, F. S., & Hayawi, M. J. (2020a). Epileptic Automated Detection from EEG Signal Using Statistical Features and Machine Learning Technique. June. https://doi.org/10.37200/IJPR/V24I8/PR281540 ͶǤ Azeez, R. A., Miften, F. S., & Hayawi, M. J. (2020b). Epileptic EEG Signals Classification Based on Determinant of Matrix as a Feature. 4, 461–465. ͷǤ Koelstra, S., Mühl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012). DEAP: A database for emotion analysis; Using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18–31. https://doi.org/10.1109/T-AFFC.2011.15 ͸Ǥ Kumar, N., Khaund, K., & Hazarika, S. M. (2016). Bispectral analysis of EEG for emotion recognition. Procedia Computer Science, 84, 31–35. ͹Ǥ Levenson, R. W., Ekman, P., Heider, K., & Friesen, W. V. (1992). Emotion and autonomic nervous system activity in the Minangkabau of West Sumatra. Journal of Personality and Social Psychology, 62(6), 972. ͺǤ Li, Mi, Xu, H., Liu, X., & Lu, S. (2018). Emotion recognition from multichannel EEG signals using K-nearest neighbor classification. Technology and Health Care, 26(S1), S509–S519. https://doi.org/10.3233/THC-174836 ͻǤ Li, Mu, & Lu, B.-L. (2009). Emotion classification based on gamma-band EEG. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1223– 1226.

ͳͲǤ Liu, W., Zheng, W.-L., & Lu, B.-L. (2016). Emotion recognition using multimodal deep learning. International Conference on Neural Information Processing, 521–529. ͳͳǤ Liu, Y., Sourina, O., & Nguyen, M. K. (2010). Real-time EEG-based human emotion recognition and visualization. 2010 International Conference on Cyberworlds, 262–269. ͳʹǤ Mohammadi, Z., Frounchi, J., & Amiri, M. (2017). Wavelet-based emotion recognition system using EEG signal. Neural Computing and Applications, 28(8), 1985–1990. ͳ͵Ǥ Mohsen, M. M., & Miften, F. S. (2021). Human Emotion Perception Based on K-Nearest Neighbors Classifier. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(13), 3670–3681. ͳͶǤ Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Zunaidi, I., & Hazry, D. (2007). EEG feature extraction for classifying emotions using FCM and FKM. International Journal of Computers and Communications, 1(2), 21–25. ͳͷǤ Raghav, G. K., Nongmeikapam, K., Dixit, A., Bose, S., & Singh, D. (2018). Evaluating Classifiers for Emotion Signal on DEAP Dataset. December, 36–42. ͳ͸Ǥ Thammasan, N., Fukui, K., & Numao, M. (2016). Application of deep belief networks in eeg-based dynamic music-emotion recognition. 2016 International Joint Conference on Neural Networks (IJCNN), 881–888. ͳ͹Ǥ Xu, H., & Plataniotis, K. N. (2016). Affective states classification using EEG and semisupervised deep learning approaches. 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), 1–6. ͳͺǤ Zhang, J., Chen, M., Zhao, S., Hu, S., Shi, Z., & Cao, Y. (2016). ReliefF-based EEG sensor selection methods for emotion recognition. Sensors, 16(10), 1558. ͳͻǤ Zhu, J.-Y., Zheng, W.-L., Peng, Y., Duan, R.-N., & Lu, B.-L. (2014). EEG-based emotion recognition using discriminative graph regularized extreme learning machine. 2014 International Joint Conference on Neural Networks (IJCNN), 525–532. ʹͲǤ Abdel-Reheim, E. S. (2016) . “Cardioprotective Efficacy of Taurine on Lipid- Metabolism of Isoproterenol induced Myocardial Infraction .” International Journal of Pharmacy and Pharmaceutical Sciences., 8:135-141 Abi, B. G .; Mohamed, S. A .; Sivakumar, V. and Jaya, S. R. (2014) . “Cardioprotective Activity of Methanolic Extract of Croton Sparciflorus on Isoproterenol Induced Wister Albino Rats.” J Med Plants Stud., 2:1-8. –76. Auda, M. A.; Majid, A. and Al-Fartosi, K. G. (2018). Protective role of polyphenolic compounds extracted from cyperus rotundus rhizomes and taurine on troponin-I and some oxidant/antioxidants parameters of female rats treated with isoproterenol – induced myocardial infraction. Journal of Thi-Qar University., 13(2): 61-76. Bishop, M. L.; Fody, E. P. and Schoeff, L. (2005). “Clinical Chemistry:Principles, Procedures, Correlations.” 5th Edn. eds.Lippincott Williams and Wilkins.Philadelphia., 200–202,285,290, 370,372,503–508. Dang, G. K.; Parekar, R. R.; Kamat, S. K.; Scindia, A. M. and Rege, N. N. (2011). “AntiinFlammatory Activity of Phyllanthus Emblica, Plumbago Zeylanica and Cyperus Rotundus in Acute Models of Inflammation.” Phytother. Res., 25:904–908. Gayon, T. A. (1972). “Plant Phenolic.” Oliver and Boyed, Edinboura.,p: 254. Jagadeesh, G. S.; Meeran, M. F. N. and Selvaraj, P. (2016). “Activation of β1-Adrenoceptor Triggers Oxidative Stress Mediated Myocardial Membrane Destabilization in Isoproterenol Induced Myocardial Infarcted Rats: 7-Hydroxycoumarin and Its Counter Action.” European Journal of Pharmacology., 777: 70–77. Kesarwani, N. and Azmi, L . (2014). “Evaluation of Cardioprotective Effect of Tinospora Cordifolia against Isoprenaline Induced Myocardial Infraction in Rats.” International Journal of Current Microbiology and Applied Sciences., 5: 543–55. Kulkarni, J. M. and Swamy, A. V. (2015). “Cardioprotective Effect of Gallic Acid against

Doxorubicin-Induced Myocardial Toxicity in Albino Rats.” Indian J Health Sci., 8: 28–35. Majid, A.: Auda, M. A. and Al-Fartosi, K. G. (2018). Cardioprotective Activity of Polyphenolic Extract of Tubers of Cyperus Rotundus and Taurine against Isoproterenol - induced Myocardial Infraction in Rats: Troponin-I and Histological Findings. Journal of Global Pharma Technology. 10(03):479-487 Okigbo, R. N.; Anuagasi, C. L.; Amadi, J. E. and, Ukapabi, U. J. (2009). “Potential Inhibitory Effect of Some African Tuberous Plant Extracts on Escherichia Coli, Staphylococcus Aureus and Candida Albicans.” International Journal of Interactive Biology., 6: 91–98. Opie, L. H. (2004). “Heart Physiology from Cell to Circulation.” 4 Th Edn. Ed. Lippincott Williams& Wilkins. USA., 561. Parikh, H.; Tripathi, C. B.; Shah, P.; Pharm, V.; Ghori. M. and Goyal, R. K. (2015). “Investigation of the Cardioprotective Effects of Crataegus Oxycantha and Its Molecular Mechanism.” Curr Res Cardiol., 2:161-167. Raza, S. M.; Tomar, V. and Siddiqui, H. H. (2012). “Cardioprotective Effect of Alcoholic Extract of Cyperus Rotundus Rhizome on Isoproterenol - Induced Myocardial Necrosis in Rats.” International Journal of Pharmaceutical Sciences and Research (IJPSR)., 3: 2535–38. Sabeena, Farvin. K. H.; Anandan, R.; Kumar, S. H.; Shiny, K. S. Sankar, T. V. and Thankappan, T. K. (2004). “Effect of Squalene on Tissue Defense System in Isoproterenol-Induced Myocardial Infarction in Rats.” Pharmacol Res., 50: 231–36. Sadiya, Khwaja.; Tarique, Mahmood. and Hefazat, H. Siddiqui. (2016). “Effect of Ethanolic Extract of Cyperus Rotundus L. against Isoprenaline Induced Cardiotoxicity.”Indian Journal of Experimental Biology., 54: 670–75. Shi, M.; He, W.; Liu, Y.; Li, X.; Yang, S.; Xu, Q. (2013). “Protective Effect of Total Phenylethanoid Glycosides from Monochasma Savatieri.” Phyto Medicine., 20: 1251–55. Shivakumar, S. I.; Suresh, H. M.; Hallikeri, C. S.; Hatapakki, B. C.; Handiganur, J. S.; Sankh, K. and Shivakumar, B. (2009). “Anticonvulsant Effect of Cyperus Rotundus Linn. Rhizomes in Upaganlawar, A.; Gandhi, C. and Balaraman, R. (2009). “Antioxidant Activities of Cyperus Rotundus L. Rhizome and Areca Catechu L. Seed.” Plant Foods Hum. Nutr., 64: 75–80.