## 3 Experimental Results
Then the image signal or matrix is trained and tested by SCNN using 10 \(\times\) 10-fold cross validation. As shown in Table 4, eight of the nine subjects obtained the highest classification accuracy using the CWT-SCNN method. Furthermore, the highest average classification accuracy is obtained with the proposed CWT-SCNN method and is at least 4% higher than the other methods. In Table 5, seven of the nine subjects obtained the highest kappa value with the CWT-SCNN method. Furthermore, the highest kappa value is obtained by using the CWT-SCNN method and is about 7-10% higher than the other methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{5}{c}{**Kappa Value**} \\ \cline{2-6}  & **CNN-SAE** & **CSP** & **ACSP** & **DBN** & **CWT-SCNN** \\ \hline S1 & **0.488** & 0.312 & 0.332 & 0.302 & 0.478 \\ S2 & 0.289 & 0.192 & 0.163 & 0.218 & **0.622** \\ S3 & **0.427** & 0.206 & 0.215 & 0.209 & 0.347 \\ S4 & 0.888 & 0.907 & 0.915 & **0.929** & 0.923 \\ S5 & 0.593 & 0.632 & 0.548 & 0.648 & **0.847** \\ S6 & 0.495 & 0.521 & 0.546 & 0.567 & **0.733** \\ S7 & 0.409 & 0.507 & 0.498 & **0.545** & 0.459 \\ S8 & 0.443 & 0.798 & 0.806 & 0.774 & **0.822** \\ S9 & 0.415 & 0.724 & 0.715 & **0.731** & 0.684 \\ \hline Average & 0.547 & 0.533 & 0.526 & 0.547 & **0.657** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The kappa values for five methods. In order to obtain the significance comparison results between the proposed algorithm and other algorithms, we use a non-parametric Friedman test [47; 48] to evaluate the classification performance of the statistical significance of the algorithms in Tables 2-5. The alpha value is set to 0.05, and the number of samples is 9. For data in the Table 2, we establish the hypothesis H0: The median classification accuracy of each algorithm is the same for the MI-EEG data. A \(p\) value is 0.0147, less than 0.05 (significance value), so the H0 was rejected. It is revealed that there is a significant difference between the classification accuracy of the compared five algorithms. Using the same method, we can obtain that there is a significant difference (\(p\) = 0.0134 < 0.05) between the classification accuracy of the CWT-SCNN, CSP-SCNN, FFT-SCNN, and STFT-SCNN algorithms for data in the Table 4. Furthermore, for data in Tables 3 and 5, the impacts in the kappa values due to different algorithms are also statically significant (\(p\) = 0.015 and 0.0179, respectively). In order to compare the difference between with and without pooling layers, we add the pooling layer to the C2 and C3 layers of SCNN to form a standard CNN. Table 6 lists the output matrix and the parameters of each network layer, when training the standard CNN with a image signal of size (44, 200). Compared with the CNN, the network parameters of SCNN are reduced by half, which not only saves the calculation cost, but also shortens the training time of the network. The average training time of the CNN is 456 s, which is 45 s longer than the average training time of SCNN (each training set contains 288 trials). Figure 5a,b shows the classification accuracies and kappa values of the BCI Competition IV Datasets 2b using the CWT-SCNN and CWT-CNN methods. The MI-EEG signal is mapped to the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{4}{c}{**Mean Classification Accuracy (\%)**} \\ \cline{2-5}  & **CSP-SCNN** & **FFT-SCNN** & **STFT-SCNN** & **CWT-SCNN** \\ \hline S1 & 65.0 & 69.1 & 72.5 & **74.7** \\ S2 & 74.4 & 72.8 & 80.0 & **81.3** \\ S3 & 64.7 & 67.8 & 64.4 & **68.1** \\ S4 & 95.6 & 94.4 & **96.3** & **96.3** \\ S5 & 83.8 & 88.1 & 88.8 & **92.5** \\ S6 & 72.5 & 72.2 & 71.6 & **86.9** \\ S7 & 67.2 & 67.5 & 66.3 & **73.4** \\ S8 & **92.5** & 90.0 & 91.9 & 91.3 \\ S9 & **84.4** & 82.2 & 80.6 & **84.4** \\ \hline Average & 77.8 & 78.2 & 79.2 & **83.2** \\ \hline \hline \end{tabular}
\end{table}