## 1 Introduction
Many researchers, therefore, investigated different approaches seeking good results. [PERSON] et al. [15] used Support Vector Machine, Naive-Bayesian Parzen-Window (NBPW), and K-Nearest Neighbour (KNN) algorithms to train and classify upper limb MI tasks and rest. Features were extracted by carrying out Common Spatial Pattern and Linear Discriminate Analysis for feature extraction. Since not all of the extracted features are necessary for classification, the optimal features are selected by using the Sequential Backward Floating Selection (SBFS) method. Two datasets were employed to assess the algorithm's performance. Their proposed method achieves an accuracy of 86.50% on the Wet Gel Electrodes dataset and 60.61% on the Emotive Epoch Headset. [PERSON] et al. [16] classified upper limb movement using raw EEG signals acquired from the BCI competition-II 2003 dataset [17]. Unreduced features along with reduced features were tested on the following classifiers: Linear Discriminate Analysis, Quadratic Discriminant Analysis (QDA), KNN, Support Vector Machine, Radial Basis Function (RBF), and Naive Bayesian (NB) classifier. Consequently, the best results were achieved by Support Vector Machine, reaching an accuracy of 82.14% in both scenarios, whilst KNN's performance improved with reduced features. [PERSON] et al. [18] used EEG signals from 40 healthy people and four ML classifiers, including Support Vector Machine, Linear Discriminate Analysis, Random Forest, and KNN, to identify foot and hand movement tasks. Common Spatial Pattern features were retrieved from several window widths,ranging from 0.3 s to 2 s. The findings demonstrate that with 2 s window sizes and nine votes, the KNN can reach 78.9%. The ranking study utilizing both accuracy and area under the curve data demonstrate that the Random Forest method can consistently perform well with varying window sizes and amounts of votes. [PERSON] et al. [19] explored the effectiveness of CNN for multi-class MI-EEG data classification. They constructed augmented Common Spatial Pattern features employing pair-wise projection matrices, which deal with various frequency ranges. There are eight levels of frequency bands, with 4 Hz and 40 Hz being the starting and ending frequencies, respectively. The width of each band is selected as (3 4 7 8 11 12 13 15) Hz and the widths of each window shift are (2 2 4 5 6 6 5 5). In total, there are 60 frequency bands. Furthermore, they presented a frequency complementary feature map selection approach that limits the reliance across frequency bands, attaining an averaged cross-validation accuracy of 69.27%. Their findings show that CNN can acquire deep and discriminant structural features for EEG classification without depending on handcrafted features (handcrafted features are those that are manually engineered). [PERSON] et al. [20] employed a cascade of one-dimensional convolutional neural networks and fully connected neural networks (1D-CNN-FC) with a cooperative training method to classify background EEG and MI-EEG. Six convolutional layers and two fully connected layers comprise the 1D-CNN-FC cascade. They compared their proposed cascade to the traditional strategy for motor imagery-BCI, which included Support Vector Machine for classification and Common Spatial Pattern for feature extraction. Their experiments compare three classification tasks: Firstly, to classify pure imagery from background EEG, secondly, transitional imagery from background EEG, and lastly, to separate both pure and transitional imagery from background EEG using a joint training scheme, considering pure and transitional imagery signals as the same class. The cooperative training method outperformed traditional training in their experiments. [PERSON] et al. [21] presented a framework in which the covariance maximization method is adopted for discriminating inter-class data and this technique uses Fast Fourier Transform Energy Maps (FFTEM) to convert 1D data into 2D data and select features.