Due to their vital applications in many real-world situations, researchers are still presenting bunches of methods for better analysis of motor imagery (MI) electroencephalograph (EEG) signals. However, in general, EEG signals are complex because of their nonstationary and high-dimensionality properties. Therefore, high consideration needs to be taken in both feature extraction and classification. In this paper, several hybrid classification models are built and their performance is compared. Three famous wavelet mother functions are used for generating scalograms from the raw signals. The scalograms are used for transfer learning of the well-known VGG-16 deep network. Then, one of six classifiers is used to determine the class of the input signal. The performance of different combinations of mother functions and classifiers are compared on two MI EEG datasets. Several evaluation metrics show that a model of VGG-16 feature extractor with a neural network classifier using the Amor mother wavelet function has outperformed the results of state-of-the-art studies.