_applied_  
_sciences_  
_Article_  
A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning  
**[PERSON] 1,*\({}^{\circled{1,*}}\), [PERSON] 2,3,4\({}^{\circled{2,3,4}}\), [PERSON] 5, [PERSON] 1\({}^{\circled{1}}\), [PERSON] 6,7 and [PERSON] 2,8,9\({}^{\circled{2,8,9}}\) \begin{tabular}{r p{341.4 pt}}
1 & ATMS Lab, Advanced Technologies for Medicine and Signals, ENIS, Sfax University, Sfax 3038, Tunisia; [EMAIL_ADDRESS] \\
2 & Faculty of Engineering, Universite De Moncton, Moncton, NB E1A3E9, Canada; [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (H.H.) \\
3 & Higher Institute of Computer Sciences and Mathematics of Monastir (ISIMM), Monastir 5000, Tunisia \\
4 & Private Higher School of Engineering and Technology (ESPRIT), El Ghazela, Ariana 2083, Tunisia \\
5 & Electrical and Computer Engineering Department, Faculty of Engineering, King Abdulaziz University (KAU), Jeddah 21589, Saudi Arabia; [EMAIL_ADDRESS] \\
6 & Department of Neurology, Habib Bourisque University Hospital, Sfax 3029, Tunisia; [EMAIL_ADDRESS] \\
7 & Neuroscience Laboratory \({}^{\circled{1,*}}\)R-12-SP-19; Faculty of Medicine, Sfax University, Sfax 3029, Tunisia \\
8 & Spectrum of Knowledge Production and Skills Development, Sfax 3027, Tunisia \\
9 & School of Electrical Engineering and Electronic Engineering, University of Johannesburg, Johannesburg 2006, South Africa \\
* & Corespondence: [EMAIL_ADDRESS] or [EMAIL_ADDRESS] \\ \end{tabular}   
**Citation:** [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning. _Appl. Sci._**2021**, _11_, 9948. [[https://doi.org/10.3309/app1129948](https://doi.org/10.3309/app1129948)]([https://doi.org/10.3309/app1129948](https://doi.org/10.3309/app1129948))  
Academic Editor: [PERSON]  
Received: 22 August 2021  
Accepted: 18 October 2021  
Submitted: 25 October 2021  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. **Copyright:** 2021 by the authors. License MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). **Abstract:** Recently, Electroencephalography (EEG) motor imagery (MI) signals have received increasing attention because it became possible to use these signals to encode a person's intention to perform an action. Researchers have used MI signals to help people with partial or total paralysis, control devices such as exoskeletons, wheelchairs, prostheses, and even independent driving. Therefore, classifying the motor imagery tasks of these signals is important for a Brain-Computer Interface (BCI) system. Classifying the MI tasks from EEG signals is difficult to offer a good decoder due to the dynamic nature of the signal, its low signal-to-noise ratio, complexity, and dependence on the sensor positions. In this paper, we investigate five multilayer methods for classifying MI tasks: proposed methods based on Artificial Neural Network, Convolutional Neural Network 1 (CNN1), CNN2, CNN1 with CNN2 merged, and the modified CNN1 with CNN2 merged. These proposed methods use different spatial and temporal characteristics extracted from raw EEG data. We demonstrate that our proposed CNN1-based method outperforms state-of-the-art machine/deep learning techniques for EEG classification by an accuracy value of 68.77% and use spatial and frequency characteristics on the BCI Competition IV-2a dataset, which includes nine subjects performing four MI tasks (left/right hand, feet, and tongue).