## 3 Result
### Performance of the Proposed Model  
The experiment results are based on the following environment: CPU: Intel(R) Core(TM) i5-7300 HQ @2.50 GHz, GPU: NVIDIA GeForce GTX 1050 Ti, Operating System: Windows 10 64 bits, Matlab: 2022a. Figure 5 displays the comparative results of three classifiers, with SVM achieving the highest accuracy of 84.1%, while softmax performed the worst with only 75.3%. This illustrates that SVM showed a better generalization ability in the small sample data sets. A notable limitation of this study was the relatively small size of the dataset, as deep learning models typically require a larger training dataset to fully demonstrate their capabilities. In future work, we aim to address this limitation by either collecting our own dataset or applying appropriate data augmentation methods to increase the dataset size. Another potential limitation is the complexity of hyperparameters, as this study employed numerous parameters. Thus, further research is necessary to determine how best to appropriately tune these hyperparameters. In addition, alternative evaluation metrics could be used to assess the effectiveness of the proposed model. Figure 6 provides an ROC curve of three classifiers. Figure 7 and Table 1 present the confusion matrices and the overall results of the precision and recall values for the proposed CNN. Figure 5: Comparison of our work and different classifiers. To showcase the effectiveness of the proposed method, we conducted a comparative analysis with several state-of-the-art models. The presentation of this comparative literature is detailed in Table 2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Our** \\ \hline Precision (\(\%\)) & 85.71 \\ Recall (\(\%\)) & 81.41 \\ F1-Score (\(\%\)) & 83.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance metrics in our CNN. Figure 6: ROC curve of the Support Vector Machines (SVMs), SoftMax, and Decision trees (DT). Figure 7: Confusion matrix for the proposed model with SVM. The contrasted performance with different results are presented in Table 3. We demonstrate the notable classification accuracy of our approach, surpassing most of the current state-of-the-art models. Impressively, we achieved a remarkable accuracy rate of 95.2% for subject 9, which is higher than for the other models. Additionally, our study boasts a minimal average accuracy gap of 25.4% across the nine subjects, indicating that our method effectively avoids personal irregularity and learns distinct action models associated with various MI states automatically in EEG. These findings highlight the potential of our approach to improve EEG-based classification in various research domains. Table 4 shows the classification accuracy with different feature maps as the input, CSP,FFT, and STFT, and a CWT test using BCI Competition IV Dataset 2B. The input image signal is trained and tested by the proposed CNN using 10 \(\times\) 10-fold cross-validation. Among the evaluated models, CWT-CNN demonstrates a competitive performance, which outperforms the CSP-CNN 4.1%, and FFT-CNN 4.5%, and STFT-CNN 2.2%, respectively, highlighting the efficacy of utilizing CWT for enhanced time-frequency feature extraction in EEG signal analysis. Although the average accuracy of CWT-CNN is the highest, however, for subjects 4, 7, and 9, the accuracy of CWT-CNN is lower than other models, suggesting a potential influence of individual variability on model performance.