## 5 Conclusions and Future Work  
This research has introduced a novel approach using the WGAN-GP model to generate synthetic EEG waveforms corresponding to concentrated and relaxed mental states. These waveforms can be effectively utilized in BCI applications across various domains requiring human-machine interaction. The synthetic data generated via WGAN-GP after the EEG signals are pre-processed significantly enhance machine learning model training by increasing the dataset size, leading to improved generalization and performance. Specifically, the classification accuracy when only the real dataset was used reached 92%, but this increased to 98.45% when combined with synthetic data generated via WGAN-GP. This performance surpasses state-of-the-art models, which have reported SVM accuracy at 93.71% and RF accuracy at 96.69% on the same datasets. Meanwhile, the original GAN model achieved an accuracy of 94.11% with a mix of real and synthetic data, underscoring the superior quality of synthetic data generated via WGAN-GP to augment real datasets. Notably, adding 25% synthetic data generated via WGAN-GP was sufficient to improve accuracy, while 50% proved optimal across mental states, achieving the highest classification accuracy with the proposed CNN classifier architecture.These findings highlight the WGAN-GP model's capability to generate high-quality synthetic EEG data, and these results illustrate how combining real and synthetic data enhances overall classification accuracy. Additionally, a user interface was developed to improve the usability of the generator model, enabling the synthesis of EEG waveforms corresponding to both concentration and relaxation states, beyond data visualization and classification. For ECG classification, the SVM model performed best with real ECG data, achieving an accuracy of 98%, while synthetic data alone reached 95.8%. In contrast, the RF classifier excelled with synthetic data, achieving an accuracy of 98.75%. When real and synthetic data were combined, the RF model maintained a high accuracy of 98.40%, an increase from 97% when real data alone were used. This demonstrates the robustness and quality of the synthetic data generated via the WGAN-GP model. Additionally, statistical significance was determined using the Wilcoxon signed-rank test, further emphasizing the potential of WGAN-GP for applications requiring a robust and reliable classification model.  
Future work for the WGAN-GP model will include exploring its applicability to additional biological signals, such as EMG, in order to further evaluate its generalizability  
Figure 11: Heatmap of pairwise statistical significance.  
and effectiveness. Moreover, we plan to enhance the interface in order to support direct ECG signal generation, as this process is currently performed via a Python script. Expanding the model in these directions could provide valuable datasets for advancing and refining machine learning algorithms. These advancements can significantly enhance human-technology interaction in applications such as assistive technologies, mental health monitoring, and cognitive load assessment, ultimately improving engagement and performance in interactive tasks.  
Methodology, A.V. and D.R.F.; Software, A.V.; Investigation, A.V.; Writing--original draft, A.V.; Writing--review & editing, D.R.F.; Supervision, D.R.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.