## 2 Related Work  
Recently, the DL and traditional algorithms were combined with other methods to extract meaningful information from EEG signals. For example, in [7], the authors proposed a method based on the combination of CNN with the gradient boosting (GB) algorithm. These algorithms are widely used in the fields of EEG signals, but their performance and accuracy in processing EEG signals are not satisfactory. Many researchers started investigating the potential for using various DL models for EEG signals analysis [8]. DL models, in particular CNN, can extract more robust and discriminating features [9; 10]. Other models, such as Long Short-Term Memory (LSTM) [11], the Recurrent Neural Network (RNN) [12], Deep Belief Network (DBN), and the Sparse AutoEncoder SAE [13], are useful in time series applications. Research shows that the DL technology has performed well in the field of EEG signal processing [14], which indicates that the automatically extracted features are better than the ones that are manually extracted. In [15], the authors propose a method that combines the multi-branch 3D CNN and the 3D representation of the EEG. The strength of the application of 3D CNN is that the temporal and spatial characteristics of EEG signals can be extracted simultaneously, and the relationship between them can be fully utilized. In [16], the authors explore CNN in combination with Filter Bank Common Spatial Pattern (FBCSP), a spatial filtering algorithm, and their own approach to extract temporal characteristics. In [17], four different CNN models with increasing depths are used to learn the spatial and temporal characteristics, which are then merged and sent to either an autoencoder or to a multilayer perceptron for classification. The features of the mu (8-13 Hz) and beta (13-30 Hz) bands of EEG have been explored by some researchers using Stacked AutoEncoder (SAE) and CNN for MI-EEG classification [18; 19]. In [20], the authors convert the EEG signal into images by using the Short-Term Fourier Transform (STFT). Several researchers propose DL models [21; 22; 23; 24; 25; 26; 27] to extract intermediate features and successfully merge models with different architectures. In [28], the authors propose a deep belief network method using a Restricted Boltzmann Machine (RBM) for the classification of MI. The training phase of in-DL models is difficult on small data sets because they can have millions of parameters, which usually requires huge training data. There are not many public EEG data sets available, and those that are available are limited in size. This limits the scope of applying deep networks in this area. However, techniques, such as transfer learning, are of nature to present new avenues of using deep networks that are first pre-trained on large datasets and then fine-tuned for smaller datasets. These techniques show better performance and reduce the training time of the deep models [29]. Numerous variants of the CNN models were used for the image classification with good accuracy. One of them is the merging of several CNNs for feature aggregation. Many researchers merge multiple CNN models and features [21; 22; 23; 24; 25; 26; 27; 28; 29] to extract intermediate features and merge models with architectures and have had some success. Although the researchers applied the CNN model and other DL models on the MI EEG data to obtain good accuracy values, they could not achieve major improvements over technical machine learning [13; 30]. This is due to the fact that the EEG signal is characterized by a low SNR, low spatial characteristics, and is difficult to interpret due to its non-stationarity. In this work, we propose five methods for the classification of MI-EEG signals. These methods are based on Artificial Neural Network (ANN), Convolutional Neural Network 1 (CNN1), CNN2, CNN1 with CNN2 merged, and the modified CNN1 with CNN2 merged. These methods start with a pre-processing step where the EOG channels have been removed and a bandpass filter (7-30 Hz) has been applied to the EEG data.