Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels |
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Authors: | Marwa M. Eid Fawaz Alassery Abdelhameed Ibrahim Mohamed Saber |
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Affiliation: | 1.Institute of Information and Computational Technologies CS MES RK, Almaty, Kazakhstan2 International Information Technology University, Almaty, Kazakhstan3 Al-Farabi Kazakh National University, Almaty, Kazakhstan4 Astana IT University, Nur-Sultan, Kazakhstan |
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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. |
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Keywords: | Signals metaheuristics optimization feature selection multilayer perceptron support vector machines |
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