Abstract
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).
Highlights
The human brain which consists of the brainstem, the cerebrum, and the cerebellum controls most of the activities of the body, processing, integrating, and coordinating the information it receives from the sense organs and making decisions as to the instructions sent to the rest of the body[1]
Some other set of rules is Grey Wolf Optimizer (GWO), GWO is a brand new optimization set of rules which simulates the gray wolves' management and looking way in nature [25].GWO is characterized through simplicity, flexibility, deprivation-unfastened mechanism, and neighborhood optima avoidance Because of that, it's been used in lots of studies regions withinside the closing years which include function subset selection [26], DC automobiles control [27], financial emission dispatch problems [28], picture registration [29], Radial Basis Function (RBF) networks schooling and fixing ultimate reactive electricity dispatch hassle, use the GWO set of rules to educate the MLP community
As shown in the experiments result in previous tables, we can see that the bMGWO outperformed other optimizers in nine dataset, this is due to high exploration and exploitation of bMGWO which allow it to find the best subset of feature, which confirms its robustness and reliability in classification tasks in the various dataset in finding the optimal subset of features
Summary
The human brain which consists of the brainstem, the cerebrum, and the cerebellum controls most of the activities of the body, processing, integrating, and coordinating the information it receives from the sense organs and making decisions as to the instructions sent to the rest of the body[1]. Wrappers offer greater accuracy it takes a good deal greater time (extra slowly) Such as Genetic algorithms (GA), (GAs) are randomly primarily based algorithms at the system of herbal choice underlying organic evolution. They may be implemented to many challenges, optimization, system mastering issues, and characteristic choice [18].To do wrapper Feature selection we want to make use of an optimization set of rules, the classical optimization strategies are by some means constrained in fixing the issues so that evolutionary computation (EC) algorithms are the opportunity for fixing those obstacles and trying to find the greatest answer of the issues.
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