EEG Channel Selection Using A Modified Grey Wolf Optimizer
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Jan 12, 2021
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Consider an increasingly growing field of research, Brain-Computer Interface (BCI) is to form a direct channel of communication between a computer and the brain. However, extracting features of random time-varying EEG signals and their classification is a major challenge that faces current BCI. This paper proposes a modified grey wolf optimizer (MGWO) that can select optimal EEG channels to be used in (BCIs), the way that identifies main features and the immaterial ones from that dataset and the complexity to be removed. This allows (MGWO) to opt for optimal EEG channels as well as helping machine learning classification in its tasks when doing training to the classifier with the dataset. (MGWO), which imitates the grey wolves leadership and hunting manner nature and which consider metaheuristics swarm intelligence algorithms, is an integration with two modification to achieve the balance between exploration and exploitation the first modification applies exponential change for the number of iterations to increase search space accordingly exploitation, the second modification is the crossover operation that is used to increase the diversity of the population and enhance exploitation capability. Experimental results use four different EEG datasets BCI Competition IV- dataset 2a, BCI Competition IV- data set III, BCI Competition II data set III, and EEG Eye State from UCI Machine Learning Repository to evaluate the quality and effectiveness of the (MGWO). A cross-validation method is used to measure the stability of the (MGWO).
Keywords: Brain-Computer interface, EEG signals, Channel selection, Feature selection, Grey wolf optimizer, Metaheuristics
References
Sylwester, R. (1995). A celebration of neurons: An educator's guide to the human brain. Association for Supervision and Curriculum Development, 1250 N. Pitt St., Alexandria, VA 22314 (ASCD Stock No. 1-95085).
Kay, K. N., Naselaris, T., Prenger, R. J., & Gallant, J. L. (2008). Identifying natural images from human brain activity. Nature, 452(7185), 352.
Swain, J. E., Dayton, C. J., Kim, P., Tolman, R. M., & Volling, B. L. (2014). Progress on the paternal brain: Theory, animal models, human brain research, and mental health implications. Infant Mental Health Journal, 35(5), 394-408.
Fazel-Rezai, R. (Ed.). (2011). Recent advances in brain-computer interface systems. BoD–Books on Demand.
Vaid, S., Singh, P., & Kaur, C. (2015, February). EEG signal analysis for BCI interface: A review. In 2015 Fifth International Conference on Advanced Computing & Communication Technologies (pp. 143-147). IEEE.
Taha Al-Kasasbeh, R., Salman Shamaseen, M., & Skopin, D. E. (2008). Automated detection and selection of artifacts in encephalography signals. Biomedical Engineering, 42(6), 293-301.
Amjed S. Al-Fahoum and Ausilah A. Al-Fraihat, “Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains,” ISRN Neuroscience, Hindawi Publishing Corporation, Article ID 730218, 2014.
D. Puthankattil Subha, Paul K. Joseph, Rajendra Acharya U, Choo Min Lim,” EEG Signal Analysis: A Survey,” Springer Science vol.34, pp.195–212, 2007.
Shiliang Sun and Jin Zhou,” A Review of Adaptive Feature Extraction and Classification Methods for EEG-Based Brain-Computer Interfaces,” in IEEE Joint Conference (IJCNN) on Neural Networks International, pp. 1746 – 1753, 2014.
Chizi, B., Rokach, L., & Maimon, O. (2009). A survey of feature selection techniques. In Encyclopedia of Data Warehousing and Mining, Second Edition (pp. 1888-1895). IGI Global.
Chandrashekar, G.,& Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.
Bell, D. A., & Wang, H. (2000). A formalism for relevance and its application in feature subset selection. Machine learning, 41(2), 175-195.
El-kenawy, E. S. M. T. (2018). Solar Radiation Machine Learning Production Depend on Training Neural Networks with Ant Colony Optimization Algorithms. IJARCCE, 7(5). doi: DOI10.17148/IJARCCE.2018.751.
Tubishat, M., Abushariah, M. A., Idris, N., & Aljarah, I. (2019). Improved whale optimization algorithm for feature selection in Arabic sentiment analysis. Applied Intelligence, 49(5), 1688-1707.
Das, S. (2001, June). Filters, wrappers and a boosting-based hybrid for feature selection. In Icml (Vol. 1, pp. 74-81).
Yang, C. H., Chuang, L. Y., & Yang, C. H. (2010). IG-GA: a hybrid filter/wrapper method for feature selection of microarray data. Journal of Medical and Biological Engineering, 30(1), 23-28.
A. M. Turing, “Computing machinery and intelligence,” Mind, 59: 433-460, 1950.
Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016). Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172, 371-381.
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Aljarah I, Faris H, Mirjalili S (2018) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput 22(1):1–15
Emary, E., Zawbaa, H. M., Grosan, C., & Hassenian, A. E. (2015). Feature subset selection approach by gray-wolf optimization. In Afro-European Conference for Industrial Advancement (pp. 1-13). Springer, Cham.
El-Kenawy, E. S., & Eid, M. Hybrid Gray Wolf And Particle Swarm Optimization For Feature Selection.
Hassib, E. M., El-Desouky, A. I., Labib, L. M., & El-kenawy, E. S. M. (2020). WOA+ BRNN: An imbalanced big data classification framework using Whale optimization and deep neural network. soft computing, 24(8), 5573-5592.
Fouad, M. M., El-Desouky, A. I., Al-Hajj, R., & El-Kenawy, E. S. M. (2020). Dynamic group-based cooperative optimization algorithm. IEEE Access, 8, 148378-148403.
Ibrahim, A., El-kenawy, E. S. M. (2020). Image Segmentation Methods Based on Superpixel Techniques: A Survey. Journal of Computer Science and Information Systems,15 (3 October 2020).
MADADI A., MOTLAGH M.M. Optimal control of DC motor using grey wolf optimizer algorithm. Technical Journal of Engineering and Applied Sciences. 2014, 4(4), pp. 373?379.
El-kenawy, E. S. M. T. (2019). A Machine Learning Model for Hemoglobin Estimation and Anemia Classification. International Journal of Computer Science and Information Security (IJCSIS), 17(2).
RATHEE P., GARG R., MEENA S. Using grey wolf optimizer for image registration. International Journal of Advance Research in Science and Engineering. 2015, 4(4), pp. 360?364.
Ibrahim, A., El-kenawy, E. S. M. (2020). Applications and Datasets for Superpixel Techniques: A Survey. Journal of Computer Science and Information Systems,15 (3 October 2020).
E. Rashedi, Nezamabadi-pour H: Feature subset selection using improved binary gravitational search algorithm, J. Intell. Fuzzy Syst. 26 (2014) 1211–1221.
E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, BGSA: binary gravitational search algorithm, Natural Comput. 9 (2010) 727–745.
BCI Competition IV, http://www.bbci.de/competition/iv/. 2008.
El-Kenawy, E. S. M., Ibrahim, A., Mirjalili, S., Eid, M. M., & Hussein, S. E. (2020). Novel feature selection and voting classifier algorithms for COVID-19 classification in CT images. IEEE Access, 8, 179317-179335.
C. Brunner, R. Leeb, G.R. Muller-Putz, A. Schlogl, G. Pfurtscheller, BCI competition 2008–Graz data set a, in: Institute for Knowledge Discovery & Institute for Human-Computer Interfaces, Graz University of Technology, Austria, 2008, pp. 1–6.
Pfurtscheller G, Neuper C, Schlögl A, Lugger K. (1998) Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans Rehabil Eng. 6(3):316-25.
El-Kenawy, E. S. M., Eid, M. M., Saber, M., & Ibrahim, A. (2020). MbGWO-SFS: Modified binary grey wolf optimizer based on stochastic fractal search for feature selection. IEEE Access, 8, 107635-107649.
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How to Cite
[1]
Hussien, H.R., El-Kenawy, E.-S.M. and El-Desouky, A.I. 2021. EEG Channel Selection Using A Modified Grey Wolf Optimizer. European Journal of Electrical Engineering and Computer Science. 5, 1 (Jan. 2021), 17-24. DOI:https://doi.org/10.24018/ejece.2021.5.1.265.
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