## 4 Results
### EEG Results
Specifically, the table highlights the accuracy achieved using each classifier type--including SVM, RF, and CNN--when applied to the respective datasets. For [PERSON] et al. [30], accuracy values are reported from their baseline models using the same dataset, with the classifiers trained on the real dataset. The reported accuracy reflects the percentage of correct classifications made using each model on each dataset type, indicating each model's effectiveness in correctly identifying the target classes. The performance of our models (GAN and WGAN-GP) is also presented for a direct comparison, where the same dataset as that of [PERSON] et al. is used. The CNN classifier trained on real data was employed, with WGAN-GP's CNN classifier demonstrating notably higher accuracy than the [PERSON] et al. [30] baseline models across real, synthetic, and combined data. This comparison serves to assess the generalization capability and robustness of each model, especially WGAN-GP's effectiveness in generating data that closely align with real data patterns. Figure 6: EEG plot of TP9 channel for Subject A in concentration and relaxation states using WGAN-GP. Figure 7: PSD plot of TP9 channel for Subject A in EEG concentration and relaxation states. The WGAN-GP model achieves the highest accuracy, with 98.42% for synthetic data and 98.45% for combined data, demonstrating its ability to generate high-quality synthetic EEG data that enhanced classification performance from 92% using only real data. In contrast, the basic GAN model achieved accuracy of 94.11%, surpassing the [PERSON] et al. [30] SVM classification accuracy of 93.71%, but falling short of their random forest (RF) classification, which reached 96.69% for combined data. While GPT-2 provides a creative approach to generating synthetic data, its lower accuracy compared to WGAN-GP suggests that it struggles to capture the nuances of EEG signals. Our findings highlight the effectiveness of WGAN-GP in addressing data scarcity, improving classification accuracy from 92% to 98.45%. Overall, WGAN-GP emerges as a superior option for applications requiring the precise classification of complex biological signals. Tables 4 and 5 analyze the classification performance with varying percentages of synthetic data, providing important insights into optimizing model accuracy. For the WGAN-GP model, integrating synthetic data significantly improves performance, with average accuracies for concentration and relaxation consistently increasing as the proportion of synthetic data rises from 25% to 50%. At 50%, the model achieves an average accuracy of 98.48%, representing a peak in performance. However, introducing 75% synthetic data results in a slight decline before improving again to 100%, suggesting diminishing returns beyond the 50% threshold. In contrast, the GAN model exhibits a decline in accuracy as the proportion of synthetic data increases, particularly when transitioning from 25% to 50%. The optimal performance of the GAN was observed at 25% synthetic data, indicating a notable disparity between the two models. This suggests that the synthetic data generated via the GAN may not be of the same quality as that produced via WGAN-GP. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Original Data + X\% Synthetic Data** & **Concentration** & **Relaxation** & **Average** \\ \hline
25\% & 99.27 & 97.28 & 98.28 \\
50\% & 99.59 & 97.36 & 98.48 \\
75\% & 99.34 & 97.26 & 98.3 \\
100\% & 99.51 & 97.39 & 98.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average classification accuracy(%) for mental states—concentration and relaxation—for varying synthetic data percentages added to the original dataset for a WGAN-GP model.