## 1 Introduction  
A brain-computer interface (BCI) is a direct communication and control system that is established between the human brain and an electronic device [1, 2]. BCI systems have important application value in many fields, especially in the field of medical treatment [3]. Various electroencephalogram (EEG) signals have been used in BCI systems, such as P300 potentials [4, 5], steady state visual evoked potentials (SSVEP) [6, 7], and motor imagery (MI) [8, 9]. Among these EEG signals, the MI signal is one of the most common signals, as it can be generated spontaneously without any stimulation. However, the recognition of MI-EEG is often difficult for several reasons. First, the high-dimensional MI-EEG signal is too weak and its signal-to-noise ratio is low [10]. Secondly, the MI-EEG signal is a nonlinear and non-stationary signal, which means that its parameters, such as mean and variance change along with time [11]. Further, MI signals are time-varying signals that depend on time variables [12]. In general, MI-EEG signals are highly complex and unstable signals, resulting in challenges for MI-EEG feature extraction and classification. Feature extraction plays a crucial role in the recognition of MI-EEG signals. However, feature extraction is often used in conjunction with preprocessing methods. The selection of preprocessing methods has an important impact on the extraction from the original MI-EEG to the effective features. Traditional methods usually use energy features, and employ preprocessing methods, such as frequency or temporal filtering, to map the raw MI-EEG data into energy signals [13; 14; 15]. [PERSON] et al. [16] used a spatial filter to map the MI-EEG data to an energy signal containing the most obvious features. [PERSON] et al. [17] extracted the time domain energy and spatial location features directly from the raw EEG. [PERSON] et al. [18] applied layer-wise relevance propagation (LRP) and deep neural networks (DNNs) to convert the MI-EEG into frequency energy characteristics. [PERSON] and [PERSON] et al. [19] used a one-versus-rest filter to analyze the MI-EEG signal, and then extract the spatial and temporal features. [PERSON] and [PERSON] et al. [20] used wavelet packet transform (WPT) to decompose and reconstruct the MI-EEG to obtain mu rhythm and beta rhythm energy feature information. Recently, a time-frequency analysis method mapped MI-EEG signals to time-frequency image signals. [PERSON] and [PERSON] et al. [21] mapped the MI-EEG signals to temporal-frequency image signals using fast Fourier transform (FFT). [PERSON] and [PERSON]. [22] employed short time Fourier transform (STFT) to perform temporal-frequency analysis on MI-EEG. Although FFT and STFT have been used to map MI-EEG to time-frequency images, FFT cannot fully capture the details of the signal and the window of STFT cannot change with the frequency. Furthermore, FFT and STFT were difficult to balance both global and local features when dealing with nonlinear unsteady MI-EEG signals [23]. In this study, we use the continuous wavelet transform (CWT), which can solve these problems by decomposing the signal into different segments and provide a window that changes with the frequency, with a high time resolution. Deep learning has a strong ability to handle complex and nonlinear high-dimensional data, and it allows machines to learn the characteristics or classify the input data [24]. Deep learning has been successfully applied to pattern recognition, especially natural language processing, computer vision, and speech recognition [25; 26; 27; 28]. Due to the ability of excellent self-learning characteristics [29; 30; 31], deep learning has gradually been applied to the identification of EEG data, such as P300 [32; 33], SSVEP [34], and MI [35]. [PERSON] et al. [36] used three-channel MI-EEG as the input of the STFT, and the proposed convolutional neural network (pCNN) was trained and tested with the output data of the STFT. [PERSON] and [PERSON] et al.