## 3 Experimental Results  
In this study, 320 trials in the Competition IV Dataset 2b were used to test our algorithm. Table 2 shows the classification accuracy of each subject's MI-EEG data using the convolutional neural network and stacked autoencoder (CNN-SAE) [22], CSP [13], adaptive common spatial patterns (ACSP) [45], deep belief net (DBN), [46] and CWT-SCNN algorithms. Bold text indicates the highest classification accuracy for each subject. As seen from Table 2, the classification performance of deep learning algorithms, such as CWT-SCNN and DBN, is better than the traditional CSP and ACSP algorithms. Furthermore, four among nine subjects (S2, S5, S6, and S8) obtained the highest classification accuracy using the CWT-SCNN algorithm. In addition, the CWT-SCNN algorithm has the highest average classification accuracy and is approximately 5-8% higher than other algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Short Name** & **Layer (Type)** & **Output Shape** & **Parameter** \\ \hline I1 & Input layer & (44, 200, 3) & None \\ \hline \multirow{3}{*}{C2} & Convolution layer & (1, 200, 8) & 1064 \\  & Batch normalization layer & (1, 200, 8) & 4 \\  & Activation layer & (1, 200, 8) & None \\ \hline \multirow{3}{*}{C3} & Convolution layer & (1, 20, 16) & 1296 \\  & Batch normalization layer & (1, 20, 16) & 64 \\  & Activation layer & (1, 20, 16) & None \\ \hline F4 & Flatten layer & (none, 320) & None \\ \hline D5 & Fully connected layer & (none, 64) & 20,544 \\ \hline O6 & Output layer & (none, 2) & 130 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The output matrix size and network parameters of each layer of SCNN. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{5}{c}{**Classification Accuracy (\%)**} \\ \cline{2-5}  & **CNN-SAE** & **CSP** & **ACSP** & **DBN** & **CWT-SCNN** \\ \hline S1 & **76.0** & 66.6 & 67.5 & 66.6 & 74.7 \\ S2 & 65.8 & 57.9 & 55.4 & 62.5 & **81.3** \\ S3 & **75.3** & 61.3 & 62.2 & 60.0 & 68.1 \\ S4 & 95.3 & 94.0 & 94.7 & **96.8** & 96.3 \\ S5 & 83.0 & 80.6 & 76.9 & 82.0 & **92.5** \\ S6 & 79.5 & 75.0 & 75.9 & 77.4 & **86.9** \\ S7 & 74.5 & 72.5 & 71.3 & **76.6** & 73.4 \\ S8 & 75.3 & 89.4 & 89.4 & 88.8 & **91.6** \\ S9 & 73.3 & 85.6 & 81.3 & **86.0** & 84.4 \\ \hline Average & 77.6 & 75.9 & 75.0 & 77.4 & **83.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The classification results for five methods. Convolutional neural network and stacked autoencoder (CNN-SAE) [22], common spatial pattern (CSP) [13], adaptive common spatial patterns (ACSP) [45], deep belief net (DBN). The kappa value is used to evaluate the classification performance of the algorithm and remove the impact of random classification [22]. The calculation expression of the kappa coefficient is as follows:  
\[\text{kappa}=\frac{acc-p_{e}}{1-p_{e}} \tag{12}\]  
Since two classification problems are studied here, the random classification accuracy in Equation (10) is (\(P_{e}\) = 0.5). Table 3 shows the kappa values using the CNN-SAE [22], CSP [13], ACSP [45], DBN [46], and CWT-SCNN algorithms. As seen from Table 3, compared with traditional algorithms, such as CSP [13] and ACSP [45], random classification has a smaller impact on deep learning algorithms, such as CWT-SCNN. Four of the nine subjects achieved the highest kappa values with the proposed algorithm. Between these, three subjects had kappa values above 0.8 with the proposed algorithm. The highest kappa value of the proposed algorithm for S4 is 0.923, slightly less than that of DBN. In addition, the CWT-SCNN algorithm has the highest average classification accuracy, and is approximately 11-13% higher than other algorithms. Tables 4 and 5 show the classification accuracies and kappa values of CSP-SCNN, FFT-SCNN, STFT-SCNN, and CWT-SCNN on the BCI Competition IV Dataset 2b. The MI-EEG signal is mapped to the time-frequency image signal using FFT, STFT, and CWT. When using traditional CSP, a matrix that maximizes the difference between the two types of features can be obtained.