Research Article
BibTex RIS Cite
Year 2021, , 120 - 125, 30.12.2021
https://doi.org/10.36222/ejt.873351

Abstract

References

  • 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.

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

Year 2021, , 120 - 125, 30.12.2021
https://doi.org/10.36222/ejt.873351

Abstract

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.

References

  • 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.
There are 10 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Research Article
Authors

Mesut Şeker 0000-0001-9245-6790

Mehmet Siraç Özerdem 0000-0002-9368-8902

Publication Date December 30, 2021
Published in Issue Year 2021

Cite

APA Şeker, M., & Özerdem, M. S. (2021). EEG Channel Selection using Differential Evolution Algorithm and Particle Swarm Optimization for Classification of Odorant-Stimulated Records. European Journal of Technique (EJT), 11(2), 120-125. https://doi.org/10.36222/ejt.873351

All articles published by EJT are licensed under the Creative Commons Attribution 4.0 International License. This permits anyone to copy, redistribute, remix, transmit and adapt the work provided the original work and source is appropriately cited.Creative Commons Lisansı