## 4 Discussion  
In this study, the proposed CWT-SCNN algorithm is used to identify the left and right hand MI-EEG signals. After being simply filtered, the EEG signals are mapped to image signals through the CWT. Then the signals are input into the SCNN for feature extraction and classification. Tested by the BCI Competition IV dataset 2b, the average classification accuracy and average kappa value obtained by CWT-SCNN algorithm are 83.2% and 0.651, respectively. Compared with the CSP-SCNN, FFT-SCNN, STFT-SCNN, and CWT-SCNN methods, the results show that the average classification accuracy and average kappa value of the CWT-SCNN method are the best. Furthermore, the experimental results show that the CWT-SCNN method not only has a higher average classification accuracy and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Short Name** & **Layer (Type)** & **Output Shape** & **Parameter** \\ \hline \hline I1 & Input layer & (44, 200, 3) & None \\ \hline \multirow{3}{*}{C2} & Convolution layer & (1, 200, 8) & 1064 \\  & Batch normalization layer & (1, 200, 8) & 32 \\  & Activation layer & (1, 200, 8) & None \\ \hline \hline P3 & MaxPooling layer & (1, 100, 8) & None \\ \hline \multirow{3}{*}{C4} & Convolution layer & (1, 91, 16) & 1296 \\  & Batch normalization layer & (1, 91, 16) & 64 \\  & Activation layer & (1, 91, 16) & None \\ \hline P5 & MaxPooling layer & (1, 46, 16) & None \\ \hline F6 & Flatten layer & (none, 736) & None \\ \hline D7 & Fully connected layer & (none, 64) & 47,168 \\ \hline O8 & Output layer & (none, 2) & 130 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The output matrix size and network parameters of each layer of the formed standard CNN. Figure 5: The mean classification accuracies and mean kappa values of the CWT-SCNN and CWT-CNN methods are shown in (**a**,**b**), respectively. average kappa value, but also has a shorter training time than that of the CWT-CNN method. In short, compared with traditional or deep learning classification methods, the CWT-SCNN method not only improves the classification accuracy and kappa value, but also shortens the training time. In order to improve the performance of BCI systems, we proposed combining the CNN and SCNN methods to identify MI-EEG signals. As can be seen from Tables 2 and 3, compared with the traditional classification algorithms, CSP or ACSP, the CWT-SCNN method improves each subject's classification accuracy and kappa value. Compared with the deep learning algorithms, CNN-SAE or DBN, the CWT-SCNN method obtains a higher average classification accuracy and average kappa value. In general, compared with traditional or deep learning classification algorithms, the CWT-SCNN method improves not only the classification performance but also the overall performance of the system. By comparing the classification results and kappa values of different preprocessing methods, CWT is more suitable for coordinating with SCNN to analyze MI-EEG signals than CSP, FFT, and STFT. As shown in Tables 4 and 5, compared with the CSP-SCNN, FFT-SCNN, and STFT-SCNN methods, the CWT-SCNN obtains higher classification results and a higher kappa of CWT-SCNN. The previous works showed that CSP as a linear analysis method may ignore short-term changes in the signal and fail to capture the details of the signal change [49]. Furthermore, FFT cannot capture the local features of MI-EEG signals well [23]. As the window size of STFT is fixed, it can not make the overall and local features clear. CWT can balance both global and local features by decomposing the signal and providing a time-varying window with a high temporal resolution [50]. These may indicate that the CWT method added on SCNN can enhance the classification performance for the MI-EEG signals. The proposed SCNN framework takes advantage of the feature extraction and classification. As can be seen from Figure 5a,b, compared with the CWT-CNN method, the CWT-SCNN method has a higher classification accuracy as well as a higher kappa value.