## 1 Introduction
FFTEM is used as a feature selection method, which exploits both the spectral and energy information hidden in EEG data. It is applied to the discriminative features obtained from Common Spatial Pattern filters to compute energy maps corresponding to frequency bands and EEG channels. These energy maps are used to train the CNN classifier. It was necessary to convert 1D data to 2D as they were using 2D CNN. Their proposed classification system achieved a mean kappa value of 0.61%. [PERSON] et al. [22] applied a CNN architecture for classification as well as a novel temporal representation of the data. Their new representation is developed by adjusting the filter-bank technique, and the CNN is constructed and optimized for the representation accordingly. On the BCI competition IV-2a dataset, their approach surpassed the recent methods in the literature by a 7% improvement in average subject accuracy. [PERSON] et al. [23] suggested a three-dimensional visualization approach as well as a multi-branch three-dimensional CNN to solve MI classification challenges. Their three-dimensional CNN representation is created by converting EEG data into a series of 2-dimensional arrays that maintain the sampling electrodes' spatial distribution. Their innovative experiments demonstrated that the introduced method achieves state-of-the-art classification kappa score levels and considerably surpasses other classifiers by a 50% reduction in various subjects' standard deviation. Furthermore, they proved that the multi-branch architecture may reduce overfitting when the size of the dataset is insufficient. [PERSON] et al. [24] presented a new semi-supervised cartesian k-means for MI-EEG classification. They employed CNN architectures that had been pre-trained on MI examples to produce deep features, which they then used to classify MI data. Deep features are the high-level features that are extracted by the feature extraction block of the CNN. Their experimental results show that their suggested strategy outperforms other comparable algorithms. [PERSON] et al. [25] presented the Temporary Sparse Group Lasso (TSGL) EEGNet neural network algorithm, which has superior efficiency on the MI-EEG and is easily interpretable. Their suggested algorithm was improved on the basis of a well-recognized DL model, known as EEGNet,and it was optimized using the traditional ML approach. Their experimental findings indicated that their approach obtained an average accuracy of 81.34% and a kappa of 75.11% on the BCI Competition IV 2a dataset [9]. [PERSON] et al. [26] evaluated the influence of the augmentation procedure's effect on the MI-EEG's classification performance rather than employing a previous transformation (Common Spatial Pattern). Furthermore, they redesigned the DNN topology to improve classification results, reduce the number of nodes in the architecture, and employ fewer hyperparameters. Moreover, they examined these enhancements on datasets from BCI competitions in 2005 and 2008, with two and four classes, respectively, and found that the augmentation had a significant influence on the average performance. The above-cited techniques remain relatively practical, due to their drawbacks, and functioning mode. BO algorithm overcomes those limitations due to its processing manner [27]. The BO concept is based on the assumption function, which holds predicted values, whereas the acquisition function helps predict the next point until reaching the global optimum. The posterior probability related to the Gaussian distribution of the unknown optimized objective function is obtained from prior information and sample data. It is effective and precise on complex training and high-dimensional data. Further, it reduces time cost and delivers accurate results. [PERSON] et al. [28] studied the effect of hyperparameter tuning on an ML algorithm employing BO with Gaussian Process (GP). They used the same dataset on other optimization algorithms, such as Random Forest and multi-grained cascade forest. As a result, the BO algorithm outperformed the other methods.