EEG Channel Selection using Differential Evolution Algorithm and Particle Swarm Optimization for Classification of Odorant-Stimulated Records

Yıl 2021, Cilt: 11 Sayı: 2, 120 - 125, 30.12.2021

Mesut Şeker Mehmet Siraç Özerdem

https://doi.org/10.36222/ejt.873351

Öz

A significant advancement has been made in the evolutionary computing and swarm intelligence methods in past decades. These methods have been commonly used to calculate well optimized solutions. Methods select the best elements or cases among set of alternatives. In EEG signal processing applications, efficient channel selection algorithms are required to reduce high dimensionality and remove redundant features. To do this, we examined optimal 5 electrodes out of 14 using Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DEA). The proposed work is related with pleasant-unpleasant EEG odors classification problem. Classification error rates were calculated by Linear Discriminant Analysis (LDA), k-NN (k Nearest Neighbor), Naive Bayes (NB), Regression Tree (RegTree) classifiers and used as fitness function for optimization algorithms. The results showed that PSO with selected 5 channels gave lowest error rates compared with DEA for all runs. RegTree classifier generated optimal fitness function value among other classifiers. PSO algorithm can effectively support channel selection problem to identify the best channels to maximize classification performance.

Anahtar Kelimeler

DEA, EEG Channel Selection, evolutionary computing, PSO, swarm intelligence

Kaynakça

• Park, S.M., Kim, J.Y., Sim, K.B. (2018). EEG electrode selection method based on BPSO with channel impact factor for acquisition of significant brain signal. Optik (Stuttg), 155, 89–96. https://doi.org/10.1016/j.ijleo.2017.10.085.
• Alotaiby, T., El-Samie, F.E.A., Alshebeili, S.A., and Ahmad, I. (2015). A review of channel selection algorithms for EEG signal processing. EURASIP J. Adv. Signal Process, 66. https://doi.org/10.1186/s13634-015-0251-9.
• Das, S., Abraham, A., and Konar, A. (2008). Particle swarm optimization and differential evolution algorithms: Technical analysis, applications and hybridization perspectives. Stud. Comput. Intell, 116, 1–38. https://doi.org/10.1007/978-3-540-78297-1_1.
• Bozorg-Haddad, O., Solgi, M., Loaiciga, H. A., Meta‐Heuristic and Evolutionary Algorithms for Engineering Optimization, First, John Wiley & Sons, Inc., New JErsey, USA, 2017. www.wiley.com.
• Satapathy, S.K., Dehuri, S., Jagadev, A.K. (2017). EEG signal classification using PSO trained RBF neural network for epilepsy identification. Informatics Med. Unlocked, 6, 1–11. https://doi.org/10.1016/j.imu.2016.12.001.
• Khushaba, R.N., Al-Ani, A., Al-Jumaily, A. (2011). Feature subset selection using differential evolution and a statistical repair mechanism. Expert Syst. Appl., 38, 11515–11526. https://doi.org/10.1016/j.eswa.2011.03.028.
• Kroupi, E., Yazdani, A., Vesin, J.-M., and Ebrahimi, T. (2014). EEG Correlates of Pleasant and Unpleasant Odor Perception, ACM Trans. Multimed. Comput. Commun. Appl., 11, 1–17. https://doi.org/10.1145/2637287.
• Acharya, U. R., Fujita, H., Sudarshan, V. K., Bhat, S., and Koh, J. E. W. (2015). Application of entropies for automated diagnosis of epilepsy using EEG signals: A review. Knowledge-Based Syst., 88, 85–96.
• O. Aydemir and T. Kayikcioglu, “Comparing common machine learning classifiers in low-dimensional feature vectors for brain computer interface applications,” Int. J. Innov. Comput. Inf. Control, vol. 9, no. 3, pp. 1145–1157, 2013.
• Gonzalez, A, Nambu, I., Hokari, H., Wada, Y. (2014). EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials. Sci. World J., 2014, 350270. https://doi.org/10.1155/2014/350270.