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Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

Marwa M. Eid1,*, Fawaz Alassery2, Abdelhameed Ibrahim3, Mohamed Saber4
1 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
2 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt
4 Department of Electronics and Communications Engineering, Faculty of Engineering, Delta University for Science and Technology, Mansoura, 11152, Egypt
* Corresponding Author: Marwa M. Eid. Email:

Computers, Materials & Continua 2022, 71(3), 4627-4641. https://doi.org/10.32604/cmc.2022.024043

Received 01 October 2021; Accepted 05 November 2021; Issue published 14 January 2022

Abstract

Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying essential and irrelevant characteristics in a dataset, and the complexity to be eliminated. This enables (SFS-Guided WOA) algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset. The (SFS-Guided WOA) algorithm is superior in performance metrics, and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

Keywords

Signals; metaheuristics optimization; feature selection; multilayer perceptron; support vector machines

Cite This Article

M. M. Eid, F. Alassery, A. Ibrahim and M. Saber, "Metaheuristic optimization algorithm for signals classification of electroencephalography channels," Computers, Materials & Continua, vol. 71, no.3, pp. 4627–4641, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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