## 1 Introduction  
The brain is composed of quite a lot of neurons that control emotions, memories, thoughts, and activities [1, 2]. Brain activity is typically produced by the electrical activity of human neurons [3]. Thus, we can measure electrical signals, which we call electroencephalograms (EEGs), or magnetic signals, which we call magnetencephalograms (MEGs) [4]. Functional magnetic resonance, acoustic, spectroscopy, computed tomography, and positron emission tomography equipment measure brain signals using magnetic, optical, and gamma rays, respectively [5, 6, 7, 8, 9]. A brain-computer interface (BCI) is a kind of artificial system manipulating objects with signals generated [10]. Motor imagery is a type of virtual interface that can analyze neuronal activity to control external devices without using the hands or feet [11, 12]. Current BCI systems can be utilized to control artificial bodies, such as electrical hand prostheses or artificial legs [13]. Therefore, it is crucial to analyze and classify brain signals with high accuracy. A BCI system includes several parts, including signal acquirement, signal processing, and application interfaces [14]. Preprocessing, feature extraction, and classification are the three major steps in signal processing [15]. A directly received brain signal is called a raw EEG. It is necessary to select the necessary brain signals and filter out unwanted noise signals coming from the measurement instruments and electrodes during preprocessingbecause the raw EEG is very weak, and the sensitivity is also low [16]. In addition, the EEG signal measurement is processed during a certain period with a determined measured paradigm from multiple electrodes; therefore, it is impossible to use raw data without signal processing [17]. Consequently, a feature extraction process extracts the appropriate features and classification processes that classify the processed signals after preprocessing [18]. Therefore, the recognition of raw EEG signal processing in motor imagery in BCI systems is a very important research topic. In the classification process, linear discriminant analysis (LDA) or a support vector machine (SVM) is generally used for the motor imagery of BCI systems [19; 20]. LDA is used to reduce a dimensional that maximizes the class variances followed by a Gaussian distribution and is efficient for low computation capabilities [21]. SVM is a classification technique based on fundamental statistical learning theory; therefore, it is relatively suitable for complex classification problems, even in small datasets [22]. In the SVM method, the hyperplane is not only the boundary that divides the datasets but also the farthest from the nearest training samples to have as much space as possible between the datasets [23]. Because of this characteristic, SVM is sometimes called large-margin classification. In the feature extraction processes, the fast Fourier transform (FFT), principal component analysis (PCA) or common spatial pattern (CSP), independent component (ICA), and eigenface analysis (EFA) methods have been widely used to provide training data and extract features from test data [24]. FFT or wavelet transform is also used to extract different features based on power spectral density [25; 26]. A short-time Fourier transform is a method that divides several frames from a time-varying signal into a window frame [27]. The Morlet wavelet transform is used to detect and analyze time-varying signals to obtain power spectrum data [2]. The Hilbert-Huang transform (HHT), which is a type of Fourier transform method, utilizes arbitrary and analytic signals obtained from data and calculates the coefficient of the FFT [27]. Fundamental PCA research started as a geometric optimization problem to find the most suitable straight line or plane for data scattering in a complex multi-dimensional space and was utilized to find the principal component that maximizes the variance of the variable [28].