## 4 Discussion  
To enhance BCI system performance, we proposed a parallel novel CNN architecture that inputs EEG signal maps transformed by CWT. Results on the left and right hand \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \hline
**Subject No.** & **CSP-CNN** & **FFT-CNN** & **STFT-CNN** & **CWT-CNN** \\ \hline
1 & 65.9 & 68.2 & 73.5 & **75.1** \\
2 & 65.1 & 63.5 & 68.1 & **69.8** \\
3 & 65.2 & 67.9 & 65.2 & **70.5** \\
4 & 91.8 & 92.1 & **93.1** & 92.6 \\
5 & 81.7 & 84.2 & 85.7 & **91.7** \\
6 & 80.31 & 78.4 & 84.3 & **87.2** \\
7 & **82.1** & 73.7 & 78.1 & 81.5 \\
8 & 92.4 & 90.0 & 91.9 & **93.3** \\
9 & **95.3** & 90.1 & 92.7 & 95.2 \\ Mean & 79.9 & 79.6 & 81.9 & **84.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Compare the result with different input. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \hline
**Subject No.** & **CEMD-CNN [4]** & **CSE-UAEL [5]** & **MFESF [6]** & **DA-CNN [7]** & **Our** \\ \hline
1 & 80.56 & 78.13 & 75.3 & 80.5 & 75.1 \\
2 & 65.44 & 54.69 & 65.4 & 70.6 & 69.8 \\
3 & 65.97 & 53.13 & 62.2 & 85.6 & 70.5 \\
4 & 99.32 & 94.38 & 97.8 & 94.6 & 92.6 \\
5 & 89.19 & 85.31 & 88.8 & 86.6 & 87.2 \\
6 & 86.11 & 80.31 & 87.2 & 87.6 & 87.2 \\
7 & 81.25 & 72.81 & 73.4 & 89.6 & 81.5 \\
8 & 88.82 & 78.75 & 91.9 & 95.6 & 93.3 \\
9 & 86.81 & 74.38 & 87.5 & 87.4 & 95.2 \\ Mean & 82.61 & 74.65 & 81.1 & 87.6 & 84.1 \\ Gap & 33.8 & 41.25 & 32.4 & 27.7 & 25.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Compare the result with advanced CNNs. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \hline
**Subject No.** & **TBTF-CNN [9]** & **CWT-CNN [8]** & **Our** \\ \hline
1 & 84.5 & **85.6** & 75.1 \\
2 & 63.3 & **72.8** & 69.8 \\
3 & 62.3 & **78.0** & 70.5 \\
4 & **98.1** & 95.4 & 92.6 \\
5 & 89.7 & 82.6 & **91.7** \\
6 & 85.0 & 79.8 & **87.2** \\
7 & 79.5 & **82.9** & 81.5 \\
8 & 84.7 & 85.0 & **93.3** \\
9 & 84.5 & 85.3 & **95.2** \\ Mean & 81.3 & 83.0 & **84.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Compare the results with other models using CWT and CNN. MI-EEG signals, the mean classification Precision, Recall and F1-Score obtained by CWT-CNN algorithm are 85.71%, 81.41%, 0.835, respectively. Basically, the proposed CNN model is able to process these time-frequency images and classify MI tasks with superior performance than other traditional classification models, which demonstrates the potential of combining CWT with deep learning techniques. As illustrated in Tables 3 and 5, our method exhibits superior classification accuracy across subjects compared with traditional algorithms like CSE-UAEL or MFESF. Furthermore, when contrasted with deep learning methods like CEMD-CNN or DA-CNN, our approach demonstrates a higher average classification accuracy. Among this, DA-CNN has the highest average accuracy because it uses data augmentation techniques to triple the number of training samples. For deep learning networks, increasing the number of samples is one of the effective means to improve the training accuracy, and is widely used in the field of engineering. However, EEG data are biological data, and we must carefully verify the use of biological data and its reliability, especially artificially generated biological data. In this context, the applicability of conventional data enhancement methods, such as inversion, resizing, and adding noise, to biological data needs to be further explored. Therefore, future works will develop data augmentation methods suitable for EEG data that meet both the engineering and biological requirements to optimize the performance of our model. Notably, individual subject analysis unveils diverse responses to different models, implying the influence of individual variability on model efficacy. In particular, our method showcases minimal accuracy discrepancies among subjects compared with other techniques.