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      General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution

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          Abstract

          Wavelet family and differential evolution are proposed for categorization of epilepsy cases based on electroencephalogram (EEG) signals. Discrete wavelet transform is widely used in feature extraction step because it efficiently works in this field, as confirmed by the results of previous studies. The feature selection step is used to minimize dimensionality by excluding irrelevant features. This step is conducted using differential evolution. This article presents an efficient model for EEG classification by considering feature extraction and selection. Seven different types of common wavelets were tested in our research work. These are Discrete Meyer (dmey), Reverse biorthogonal (rbio), Biorthogonal (bior), Daubechies (db), Symlets (sym), Coiflets (coif), and Haar (Haar). Several kinds of discrete wavelet transform are used to produce a wide variety of features. Afterwards, we use differential evolution to choose appropriate features that will achieve the best performance of signal classification. For classification step, we have used Bonn databases to build the classifiers and test their performance. The results prove the effectiveness of the proposed model.

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          A survey on feature selection methods

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            A review of classification algorithms for EEG-based brain–computer interfaces

            In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.
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              Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                International Journal of Distributed Sensor Networks
                International Journal of Distributed Sensor Networks
                SAGE Publications
                1550-1477
                1550-1477
                March 2020
                March 04 2020
                March 2020
                : 16
                : 3
                : 155014772091100
                Affiliations
                [1 ]Department of Computer Science, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan
                [2 ]Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, Jordan
                [3 ]College of Engineering, Al Ain University, Abu Dhabi, UAE
                [4 ]Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia
                [5 ]Department of Computer Science, COMSATs University Islamabad, Islamabad, Pakistan
                Article
                10.1177/1550147720911009
                72617244-1512-4895-a645-a507333abf8c
                © 2020

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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