Feature Normalization Effect in Emotion Classification based on EEG Signals

Yıl 2020, Cilt: 24 Sayı: 1, 60 - 66, 01.02.2020

Orhan Akbulut

https://doi.org/10.16984/saufenbilder.617642

Cited By: 3

Öz

Normalization of data
in classification-based problem is a fundamental task where binary or multi classifier
systems integrate it as a sub-system.  Normalization
can be thought as a mapping function that makes a transformation from one space
to another space. Different types of normalization methods are proposed
depending on the data content. Recently, researches are carried out on whether
this process is really necessary. In this paper, the performances of the
different normalization methods for Electroencephalogram (EEG) signal based
emotion classification are evaluated. Support vector machine based binary
classifier is used in emotion classification. Different kernel functions for support
vector machine are also considered. Although the experimental findings may not
reveal a significant performance difference between different types of
normalization, the normalization process increases classification performance
of the emotion recognition, in general.

Anahtar Kelimeler

normalization, classification, support vector machine

Kaynakça

• [1] S. Theodoridis, and K. Koutroumbas, Pattern Recognition, 4th Edition, Academic Press, 2008.
• [2] G. W. Milligan and M. C. Cooper, “A study of standardization of variables in cluster analysis,” Journal of Classification, vol. 5, pp. 181–204, 1988.
• [3] C. W. Hsu, C. C. Chang, and C. J. Lin, “A practical guide to support vector classification” Tech. Rep., 2003.
• [4] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. San Mateo, CA, USA: Morgan Kaufmann, 2006.
• [5] I. L. Fonseca, “The Impact of data normalization on unsupervised continuous classification of landforms,” International Geoscience and Remote Sensing Symposium, 2003.
• [6] L. A. Shalabi, and Z. Shaaban, “Normalization as a preprocessing engine for data mining and the approach of preference matrix,” International Conference on Dependability of Computer Systems, 2006.
• [7] M. C. P. Souto, D. S. A. Araujo, I. G. Costa, R. G. F. Soarez, T. B. Ludermir, and A. Schliep, “Comparative study on normalization procedures for cluster analysis of gene expression datasets,” IEEE International Joint Conference on Neural Networks, 2008.
• [8] T. Jayalaklashmi, and A. Santhakumaran, “Statistical normalization and back propagation for classification,” International Journal of Computer Theory and Engineering, vol. 3, no. 1, pp. 89-93, 2011.
• [9] V. R. Patel, and R. G. Mehta, “Impact of outlier removal and normalization approach in modified k-Means clustering algorithm,” International Journal of Computer Science Issues, vol. 8, no. 5, pp 331-336, 2011.
• [10] I. B. Mohamad, and D. Usman, “Standardization and its effects on K-Means clustering algorithm,” Research Journal of Applied Sciences, Engineering and Technology, vol. 6, no. 17, pp. 3299-3303, 2013.
• [11] L. Xie, Q. Tian, and B. Zoang, “Feature normalization for part-based image classification,” IEEE International Conference on Image Processing, 2013.
• [12] M. S. Azmi, N. A. Arbain, A. K. Muda, Z. A. Abas, and Z. Muslim, “Data normalization for triangle features by adapting triangle nature for better classification,” IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, 2015.
• [13] H. Zhang, H. Lin, and Y. Li, “Impacts of feature normalization on optical and SAR data fusion for land use/land cover classification,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 5, pp. 1061-1065, 2015.
• [14] B. K. Singh, K. Verma, and A. S. Thoke, “Investigations on impact of feature normalization techniques on classifier’s performance in breast tumor,” International Journal of Computer Applications, vol. 116, no. 19, pp. 11-15, 2015.
• [15] K. P. Kuntal, and K. S. Sudeep, “Preprocessing for image classification by convolutional neural networks,” IEEE International Conference on Recent Trends in Electronics Information Communication Technology, 2016.
• [16] A. S. Easa, and W. Arabo, “A Normalization Methods for Backpropagation: A Comparative Study,” Science Journal of University of Zakho, vol. 5, no. 4, pp. 319-323, 2017.
• [17] M. Kociolek, M. Strlecki, and S. Szymajda, “On the influence of the image normalization scheme on texture classification accuracy,” Signal Processing: Algorithms, Architectures, Arrangements, and Applications, 2018.
• [18] Harender, and R. K. Sharma, “DWT based epileptic seizure detection from EEG signal using k-NN classifier,” International Conference on Trends in Electronics and Informatics, 2017.
• [19] J. W. C. Medithe, and U. R. Nelakuditi, “Study of normal and abnormal EEG,” International Conference on Advanced Computing and Communication Systems, 2016.
• [20] E. K. St Louis, and L. C. Frey (Eds), “Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants”, Chicago, IL: American Epilepsy Society, 2016.
• [21] P. Ekman, “Basic emotions”, Handbook of cognition and emotion, pp. 45-60, 1999.
• [22] J. A., Russell, “A circumplex model of affect,” Journal of Personality and Social Psychology, vol. 39, no. 6, pp. 1161-1178, 1980.
• [23] M. Mikhail, K. El Ayat, J. A. Coan, J. J. Allen, “Using minimal number of electrodes for emotion recognition using brain signals produced from a new elicitation technique,” International Journal of Autonomous and Adaptive Comm. System, vol. 6, no. 1, pp.80-97, 2013.
• [24] N. Jatupaiboon, S. Pangum, and P. Israsena, “Emotion classification using minimal EEG channels and frequency bands,” 10th International Joint Conference on Computer Science and Software Engineering, pp. 21-24, 2013.
• [25] J. Zhang, M. Chen, S. Zhao, S. Hu, Z. Shi, and Y. Cao, “ReliefF-based EEG sensor selection methods for emotion recognition,” Sensors, vol. 16, no. 10, 2016.
• [26] Details omitted for double-blind reviewing.
• [27] S. Koelstra, et al., “DEAP: A Database for Emotion Analysis ; Using Physiological Signals,” IEEE Transactions on Affecting Computing, vol. 3, no. 1, pp.18-31, 2012.
• [28] R. Jenke, A. Peer, M. Buss, “Feature extraction and selection for emotion recognition from EEG” IEEE Transactions on Affective Computing, vol. 5, no. 3, pp. 327-339, 2014.