## 4 Results  
### EEG Results  
To enhance user-friendliness in synthetic EEG generation, an interface was developed using 'tkinter', enabling users to configure input and output paths without modifying the Python code, as shown in Figure 5. The interface allows users to merge real and synthetic data in varying proportions, and it includes tabs for Synthetic EEG Generation, DWT plotting, and CNN classification. Outputs from GAN and WGAN-GP models are saved as CSV files containing synthetic data for channels TP9, AF7, AF8, and TP10. These files match the original data in both data points and timestamps, ensuring seamless integration with real data for classification using the CNN classifier from [31], with results recorded per subject. The current interface is exclusively designed for synthetic EEG wave generation, while synthetic ECG generation is currently handled via a Python script. Future work aims to integrate synthetic ECG generation into the interface. Figures 6 and 7 collectively illustrate the effectiveness of the WGAN-GP model in accurately replicating real EEG patterns in both temporal and frequency domains, thereby validating the model's ability to generate realistic synthetic EEG data. In Figure 6, we observe the EEG waveforms from the TP9 channel for Subject A, presented for both concentration and relaxation states. The synthetic data, which extend seamlessly from the real EEG signals, demonstrate continuity in amplitude and oscillatory patterns, indicating that the model can produce consistent, biologically plausible waveforms. This continuity suggests that the synthetic data may be appended to real EEG data without introducing detectable discontinuities, which is essential for applications requiring prolonged EEG sequences. Figure 7 further supports the validity of the synthetic data by presenting the Power Spectral Density (PSD) of the TP9 channel for the same subject and states. In the concentration state, the PSD of the synthetic data captures prominent gamma wave activity, reflecting the heightened cognitive processing typically associated with concentration. This match in gamma power indicates that the synthetic EEG successfully mirrors the spectral characteristics of the real concentration-state EEG. Similarly, in the relaxation state, the synthetic data show minimal gamma and beta activity, consistent with the reduced cognitive and wakeful engagement expected in a relaxed state. This spectral match reinforces the evidence that the model accurately distinguishes between cognitive states, not only in waveform morphology but also in underlying spectral features. Together, Figures 6 and 7 illustrate that the WGAN-GP model produces synthetic EEG data that closely align with the real data across both waveform and spectral dimensions, substantiating the model's potential as a tool for generating realistic, state-specific EEG patterns. This high degree of fidelity in both the temporal and spectral domains demonstrates that the synthetic output of the model effectively simulates real EEG data, making it a valuable resource for research and applications requiring synthetic EEG signals. Table 2 presents the number of data points recorded for each EEG channel (TP9, AF7, AF8, and TP10) across the mental states (concentration and relaxation) for real and corresponding synthetic EEG waves for both GAN and WGAN-GP. The data show consistent values across  
Figure 5: Interface of the synthetic EEG generator, visualization, and CNN classification. all channels and subjects for both real and synthetic datasets. This consistency indicates that both models effectively generated synthetic data that matched the structure of the real data, making them suitable for further analysis. The evaluation criteria for model accuracy in Table 3 are based on comparing classification performance across different models when trained on a real dataset and tested on unseen real, synthetic, and combined datasets.