Deep neural networks (DNNs) are observed to be successful in pattern classification. However, the high classification performances of DNNs are related to their large training sets. Unfortunately, in the literature, the datasets used to classify motor imagery (MI) electroencephalogram (EEG) signals contain a small number of samples. To achieve high performances with small-sized datasets, most of the studies have employed a transformation such as the common spatial patterns (CSP) before the classification process. However, the CSP is dependent on subjects and introduces computational load in real time applications. It is observed in the literature that data augmentation is not applied for increasing the classification performance of EEG signals. In this study, we have investigated the effect of the augmentation process on the classification performance of MI EEG signals instead of using a preceding transformation such as the CSP, and we have demonstrated that the augmentation process is able to compete with the CSP by generating high success rates for the classification of MI EEGs. In addition to the augmentation process, we have modified the DNN structure to increase the classification performance, to decrease the number of nodes in the structure, and to use less number of hyper parameters. A minimum distance network following the last layer of the convolutional neural network (CNN) was used as the classifier instead of a fully connected neural network (FCNN). By augmenting the EEG dataset and focusing solely on CNN's training, the training algorithm of the proposed structure is strengthened without applying any transformation. We tested these improvements on brain-computer interface (BCI) competitions 2005 and 2008 databases with two and four classes, and the positive effects of the augmentation on the average accuracies are demonstrated. (C) 2021 Elsevier B.V. All rights reserved.