## 1 Introduction  
In recent years, the use of brain signals from EEG electroencephalography has been widely explored for various applications with a major focus on the field of biomedical engineering. A Brain-Computer Interface (BCI) system, also referred to as brain-machine interaction, bridges the gap between humans and computers by translating thoughts into commands, which can be used to communicate with external devices like exoskeletons,wheelchairs, prostheses or used for rehabilitation of sensory, motor, and cognitive disabilities [1]. Thought-based control of machines is a hot topic that is increasingly integrated into various applications. BCIs were initially developed for people with disabilities, and in particular for those suffering from what is known as \"locked-in syndrome\" [2] which is a neurological pathology. The patient with this disease is usually quadriplegic and cannot move or speak. On the other hand, his consciousness and his cognitive and intellectual faculties are intact. People suffering from complete muscular paralysis can no longer autonomously communicate with the outside world, despite their full mental capacity. The development of BCIs would allow these people to regain their autonomy of motion. Non-invasive electroencephalography (EEG) of the scalp is an easy and inexpensive technique for recording brain activity. Decoding the user's intent from EEG signals is one of the most difficult challenges in BCI. One of the main reasons is that EEG signals have non-stationary and complex properties [3]. For the MI task, it is particularly difficult to obtain high-quality data because it is not known what exactly the user has imagined. For this reason, recent advances in MI-based BCI approaches [4; 5; 6] enabled improving the decoding accuracy by using many feature extraction or classification methods based on advanced machine learning algorithms and deep learning (DL). Recently DL algorithms have been intensively developed to make a profit from the progress of artificial intelligence technologies. Successful results have been obtained in the classification of MI tasks. CNN is a class of artificial neural networks that presents one of the most commonly used DL architectures for image recognition tasks. Since CNN is specially designed to process input images, its architecture is made up of two main blocks. The first block presents the particularity of this type of neural network since it functions as a feature extractor. The second block is not an exclusive feature of CNN: it is, in fact, used at the end of all the neural networks used for classification. CNN model has low network complexity and strong feature extraction capabilities, which can well solve the problem of difficult feature extraction from EEG signals. Therefore, it is feasible to classify the MI tasks of EEG signals based on the CNN model. Our aims are oriented towards the high precision requirements of the classification of MI tasks of the EEG signal. Based on the fusion of CNNs architectures, we want to achieve an efficient classification of EEG signals from the Competition IV 2a dataset. The CNN model with the Wavelet Packet Decomposition (WPD) and Common Spatial Pattern (CSP) algorithms were used for feature extraction and classification of the MI-EEG signals. The elements of originality may be summarized as follows:  
* The performance of classifiers is improved by pre-processing of the EEG signals. We proceed with removal of EOG channels and with applying a bandpass filter. * We extracted frequential and spatial features by using WPD and CSP techniques respectively. * We showed that the proposed method based on CNN1 model gives the highest value of accuracy compared to the state-of-the-art. The rest of this paper is organized as follows. In Section 2, we briefly review related work. Section 3 presents our five proposed methods for the classification of MI tasks in EEG signals. In Section 4, we analyze the experimental results that verify the effectiveness of the proposed methods.