Abstract
The classification of electroencephalogram (EEG) signals plays a key role in detecting brain activities. Fuzzy methods are widely applied in decision-making problems because they are effective tools for handling imprecise and vague data. This paper proposes a modified algorithm to calculate the center of gravity of generalized trapezoidal fuzzy numbers. Accordingly, we introduce a new similarity measure for generalized trapezoidal fuzzy numbers that we use in the classification of EEG signals. This measure combines the height, the center of gravity, the perimeter, the area, and the gyradius of generalized trapezoidal fuzzy numbers to quantify the similarity between generalized trapezoidal fuzzy numbers. We use 16 sets of generalized trapezoidal fuzzy numbers to compare the proposed similarity measure with existing ones. Comparison results indicate that the proposed similarity measure can overcome the drawbacks of existing similarity measures. Finally, an EEG experiment is carried out in laboratory. Experimental results demonstrate that the proposed similarity measure is more effective than other methods in terms of classification of EEG signals.
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Data Availability
The dataset utilized in this paper comes from the EEG laboratory.
Notes
http://www.sda.gov.cn/WS01/CL0845/69410.html, Accessed:14.02.12
References
Ahmed, F.: Region level Bi-directional Deep Learning Framework for EEG-based Image Classification, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) IEEE, (2018)
Ezgi, O.K.: Classification of EEG signals for epileptic seizures using linear and non-linear classifiers based wavelet transforms and information criteria. Turkiye Klinikleri J. Biostat. 11, 102–122 (2019)
Satyajit, A., Sandeep, J., Pradip Kumar, G.: Epileptic seizure detection in EEG signal using discrete stationary wavelet-based Stockwell transform. Majlesi. J. Electr. Eng. 13, 55–63 (2019)
Hamwira, Y., Hazim, O., Dini, H., Raini, H.: Emotional profiling through supervised machine learning of interrupted EEG interpolation. Int. J. Adv. Comput. Res. 9, 242–251 (2019)
He, T.: Attention Training method based on EEG signals. Acta Microscopica 28, 1195–1206 (2019)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Esmeralda, Ramos: A Fuzzy Hybrid Intelligent System for Human Semen Analysis, Ibero-american Conference on AI: Advances in Artificial Intelligence. Springer, New York (2008)
Mohammad, A., Neagu, D., Cowling, P.I.: Analogy-based software effort estimation using Fuzzy numbers. J. Syst. Softw. 84(2), 270–284 (2011)
Rodriguez, Y., Morell, C., Grau, R., Garcia, M.M., Baets, B.D.: Fuzzy case-based system for classification tasks on missing and noisy data. Eighth International Conference on Hybrid Intelligent Systems (2008)
Begum, S., Ahmed, M.U., Funk, P., Xiong, N., Von Scheele, B.: A case-based decision support system for individual stress diagnosis using fuzzy similarity matching. Comput. Intell. 25(3), 180–195 (2009)
Yuvaraj, R., Rajendra Acharya, U., Hagiwara, Y.: A novel Parkinson’ s disease diagnosis index using higher-order spectra features in EEG signals. Neural Comput. Appl. 30(4), 1225–1235 (2016)
Hamze, L., Ali, M.: Application of multiscale fuzzy entropy features for multilevel subject-dependent emotion recognition. Turk. J. Electr. Eng. Comput. Sci. 27(6), 4070–4081 (2019)
Ramakrishnan, S., Muthanantha Murugavel, A.S.: Epileptic seizure detection using fuzzy-rules-based sub-band specific features and layered multi-class SVM. Pattern Anal. Appl. 22(3), 1161–1176 (2019)
Wang, P.Z.: Fuzzy Sets and Its Applications, pp. 55–58. Shanghai Science and Technology Press, Shanghai (1983)
Chen, S.J., Chen, S.M.: Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. IEEE Trans. Fuzzy Syst. 11(1), 45–56 (2003)
Rituparna, C., Mridul, G.: Krishna: Fuzzy risk analysis in poultry farming based on a novel similarity measure of fuzzy numbers. Appl. Soft Comput. 66, 60–76 (2018)
Hejazi, S.R., Doostparast, A., Hosseini, S.M.: An improved fuzzy risk analysis based on a new similarity measures of generalized fuzzy numbers. Exp. Syst. Appl. 38(8), 9179–9185 (2011)
Khorshidi, H.A., Nikfalazar, S.: An improved similarity measure for generalized fuzzy numbers and its application to fuzzy risk analysis. Appl. Soft Comput. 52, 478–486 (2016)
Li, J., Zeng, W.: Fuzzy risk analysis based on the similarity measure of generalized trapezoidal fuzzy numbers. J. Intell. Fuzzy Syst. 32(3), 1673–1683 (2017)
Hua, W.S., Chen, S.M.: A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. Exp. Syst. Appl. 36(1), 589–598 (2009)
Wen, J., Fan, X., Duanmu, D., Yong, D.: A modified similarity measure of generalized fuzzy numbers. Proc. Eng. 15, 2773–2777 (2011)
Lin, J., Chen, Q., Tian, X., Fengshou, G.: Fault diagnosis of rolling bearings based on multifractal detrended uctuation analysis and mahalanobis distance criterion. Mech. Syst. Signal Process. 38(2), 515–533 (2013)
Mitchell, H.B.: Pattern recognition using type-II fuzzy sets. Inf. Sci. 170, 409–418 (2005)
Zuo, X., Wang, L., Yue, Y.: A new similarity measure of generalized trapezoidal fuzzy numbers and its application on rotor fault diagnosis. Math. Probl. Eng. 10, 1–10 (2013)
Khorshidi, H.A., Gunawan, I., Nikfalazar, S.: Application of fuzzy risk analysis for selecting critical processes in implementation of SPC with a case study. Group Decis. Negot. 25(1), 1–18 (2015)
Patra, K., Mondal, S.K.: Fuzzy risk analysis using area and height based similarity measure on generalized trapezoidal fuzzy numbers and its application. Appl. Soft Comput. 28(C), 276–284 (2015)
Jiang, W., Yang, Y., Luo, Yu., Qin, X.: Determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers. Int. J. Comput. Commun. Control 10(3), 333–347 (2015)
Lee, C.C.: Fuzzy logic in control system: fuzzy logic controller-part I-II. IEEE Trans. SMC 20, 1–7 (1990)
Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic-Theory and Applications. Prentice-Hall Inc, Upper Saddle River (1995)
Cheng, C.H.: A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets Syst. 95(3), 307–317 (1998)
Zhangyan, X., Shang, S., Qian, W., Shu, W.: A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers. Exp. Syst. Appl. 37(3), 1920–1927 (2010)
Xin, Z., Wang, L., Yuanlong, Yu.: A new similarity measure of generalized trapezoidal fuzzy numbers and its application on rotor fault diagnosis. Math. Probl. Eng. 2013(pt.2), 824706.1–C824706.10 (2013)
Bojadziev, G., Bojadziev, M.: Fuzzy Logic for Business, Finance, and Management. World Scientific, Singapore (1997)
Wei, Z.: Relief feature selection and parameter optimization for support vector machine based on mixed kernel function. Int. J. Performab. Eng. 14(2), 280–289 (2018)
Wang, X., Li, H., Zhang, Q., Wang, R.: Predicting subcellular localization of apoptosis proteins combining go features of homologous proteins and distance weighted knn classifier. BioMed Res. Int. 2, 1–8 (2016)
Uguroglu, S., Carbonell, J., Doyle, M., Biederman, R.: Cost-sensitive risk stratification in the diagnosis of heart disease. Twenty-sixth AAAI Conf. Artif. Intell. 2335–2340, 1–8 (2012)
Schrift, R.Y., Parker, J.R., Zauberman, G., Srna, S.: Multi-stage decision processes: the impact of attribute order on how consumers mentally represent their choice. J. Consum. Res. 44, 1307–1324 (2016)
Coltuc, D., Datcu, M., Coltuc, D.: On the use of normalized compression distances for image similarity detection. Entropy 20(2), 99 (2018)
Wei, M., Dai, Q., Sun, S., Ionita, S., Voln, E., Gavrilov, A., Liu, F.: A prediction model for traffic emission based on interval-valued intuitionistic fuzzy sets and case-based reasoning theory. J. Intell. Fuzzy Syst. 31(6), 3039–3046 (2016)
Muthukumar, P., G. Sai sundara krishnan, : A similarity measure of intuitionistic fuzzy soft sets and its application in medical diagnosis. Appl. Soft Comput. 41, 148–156 (2015)
Son, L.H., Phong, P.H.: On the performance evaluation of intuitionistic vector similarity measures for medical diagnosis. J. Intell. Fuzzy Syst. 31(3), 1597–1608 (2016)
Guo, J.: Hybrid multicriteria group decision making method for information system project selection based on intuitionistic fuzzy theory. Math. Probl. Eng. 16, 1–12 (2013)
Garai, A.: Intuitionistic fuzzy t-sets based solution technique for multiple objective linear programming problems under imprecise environment. Notes on Instuitionistic Fuzzy Sets 21(4), 104–123 (2015)
Mandal, P., Roy, T.K., Garai, A.: Intuitionistic fuzzy t-sets based optimization technique for production-distribution planning in supply chain management. OPSEARCH 53, 950–975 (2016)
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Appendix
Appendix
Definition 1
If the membership function of generalized trapezoidal fuzzy number \({\widetilde{A}}=(a_1,a_2,a_3,a_4 ; w)\) is
where \(a_1,a_2,a_3,a_4\) are real values, \(a_1 \le a_2 \le a_3 \le a_4\).\(0 \le w \le 1\). Then we call \({\widetilde{A}}\) a generalized trapezoidal fuzzy number.
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Qi, Z. An Improved Similarity Measure for Generalized Trapezoidal Fuzzy Numbers and Its Application in the Classification of EEG Signals. Int. J. Fuzzy Syst. 23, 890–905 (2021). https://doi.org/10.1007/s40815-020-01043-0
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DOI: https://doi.org/10.1007/s40815-020-01043-0